no code implementations • ACL 2022 • Zuchao Li, Masao Utiyama, Eiichiro Sumita, Hai Zhao
Although this can satisfy the requirements overall, it usually requires a larger beam size and far longer decoding time than unrestricted translation, which limits the concurrent processing ability of the translation model in deployment, and thus its practicality.
no code implementations • ACL (WAT) 2021 • Zuchao Li, Masao Utiyama, Eiichiro Sumita, Hai Zhao
This paper describes our system (Team ID: nictrb) for participating in the WAT’21 restricted machine translation task.
no code implementations • EMNLP 2021 • Zuchao Li, Masao Utiyama, Eiichiro Sumita, Hai Zhao
Machine translation usually relies on parallel corpora to provide parallel signals for training.
no code implementations • WMT (EMNLP) 2021 • Zuchao Li, Masao Utiyama, Eiichiro Sumita, Hai Zhao
In this paper, we describe our MiSS system that participated in the WMT21 news translation task.
no code implementations • EMNLP (ACL) 2021 • Hai Zhao, Rui Wang, Kehai Chen
This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results.
no code implementations • Findings (EMNLP) 2021 • Jiawei Wang, Hai Zhao, Yinggong Zhao, Libin Shen
Machine reading comprehension (MRC) is a challenging NLP task for it requires to carefully deal with all linguistic granularities from word, sentence to passage.
Chinese Reading Comprehension Machine Reading Comprehension +1
no code implementations • COLING 2022 • Yifei Yang, Zuchao Li, Hai Zhao
Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
no code implementations • COLING 2022 • Yifei Yang, Hai Zhao
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • COLING 2022 • Jiawei Wang, Hai Zhao
ArT is totally unsupervised and KBs-free.
no code implementations • WMT (EMNLP) 2020 • Zuchao Li, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task.
1 code implementation • COLING 2022 • Ziming Cheng, Zuchao Li, Hai Zhao
Abstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences.
Ranked #1 on AMR-to-Text Generation on LDC2017T10 (using extra training data)
no code implementations • Findings (ACL) 2022 • Zuchao Li, Yiran Wang, Masao Utiyama, Eiichiro Sumita, Hai Zhao, Taro Watanabe
Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT.
no code implementations • EMNLP (ACL) 2021 • Zuchao Li, Kevin Parnow, Masao Utiyama, Eiichiro Sumita, Hai Zhao
With this system, we aim to provide a complete translation experience for machine translation users.
1 code implementation • 12 Mar 2024 • Tianshuo Peng, Zuchao Li, Lefei Zhang, Hai Zhao, Ping Wang, Bo Du
Large Language Models (LLMs), benefiting from the auto-regressive modelling approach performed on massive unannotated texts corpora, demonstrates powerful perceptual and reasoning capabilities.
Ranked #18 on Visual Question Answering on MM-Vet
no code implementations • 4 Mar 2024 • Yifei Yang, Tianqiao Liu, Bo Shao, Hai Zhao, Linjun Shou, Ming Gong, Daxin Jiang
Webpage entity extraction is a fundamental natural language processing task in both research and applications.
no code implementations • 26 Feb 2024 • Khai Jiet Liong, Hongqiu Wu, Hai Zhao
(2) We introduce \textit{S-Attend}, a novel smoothing technique that effectively makes SA robust via structural perturbations.
1 code implementation • 19 Feb 2024 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Large language models (LLMs) have shown remarkable potential as human-like autonomous language agents to interact with real-world environments, especially for graphical user interface (GUI) automation.
1 code implementation • 19 Feb 2024 • Zouying Cao, Yifei Yang, Hai Zhao
In this paper, we present a perspective on $\textit{$\textbf{head-wise shareable attention for large language models}$}$.
1 code implementation • 17 Feb 2024 • Yifei Yang, Zouying Cao, Hai Zhao
Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference.
1 code implementation • 17 Feb 2024 • Junlong Li, Fan Zhou, Shichao Sun, Yikai Zhang, Hai Zhao, PengFei Liu
As a relative quality comparison of model responses, human and Large Language Model (LLM) preferences serve as common alignment goals in model fine-tuning and criteria in evaluation.
no code implementations • 8 Feb 2024 • Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang, Hai Zhao, Lifeng Liu, Yulong Wang
Q3: Which knowledge features are correlated with the performance and robustness of editing?
1 code implementation • 4 Feb 2024 • Xuanchang Zhang, Zhuosheng Zhang, Hai Zhao
Despite the rapid progress of large language models (LLMs), their task performance remains sensitive to prompt design.
1 code implementation • 19 Dec 2023 • Weixi Song, Zuchao Li, Lefei Zhang, Hai Zhao, Bo Du
With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue.
1 code implementation • 13 Dec 2023 • Tianshuo Peng, Zuchao Li, Ping Wang, Lefei Zhang, Hai Zhao
However, previous methods still have certain limitations: (i) They ignore the difference in the focus of visual information between different analysis targets (aspect or sentiment).
1 code implementation • 20 Nov 2023 • Zhuosheng Zhang, Yao Yao, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao
Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks.
no code implementations • 27 Oct 2023 • Yilin Zhao, Hai Zhao, Sufeng Duan
Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options.
1 code implementation • 20 Oct 2023 • JinYuan Wang, Junlong Li, Hai Zhao
To further extend this task, we officially introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop questions with explicit reasoning steps in open-domain setting.
1 code implementation • 10 Oct 2023 • Anni Zou, Zhuosheng Zhang, Hai Zhao, Xiangru Tang
Large language models (LLMs) have unveiled remarkable reasoning capabilities by exploiting chain-of-thought (CoT) prompting, which generates intermediate reasoning chains to serve as the rationale for deriving the answer.
1 code implementation • 9 Oct 2023 • Hongqiu Wu, Linfeng Liu, Hai Zhao, Min Zhang
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data.
1 code implementation • 9 Oct 2023 • Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao, PengFei Liu
The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address.
no code implementations • 30 Sep 2023 • Zouying Cao, Yifei Yang, Hai Zhao
While Large language models (LLMs) have garnered widespread applications across various domains due to their powerful language understanding and generation capabilities, the detection of non-factual or hallucinatory content generated by LLMs remains scarce.
no code implementations • 18 Sep 2023 • Xinbei Ma, Yi Xu, Hai Zhao, Zhuosheng Zhang
On the other hand, the split segments are an appropriate element of multi-turn dialogue response selection.
1 code implementation • 8 Sep 2023 • JinYuan Wang, Hai Zhao, Zhong Wang, Zeyang Zhu, Jinhao Xie, Yong Yu, Yongjian Fei, Yue Huang, Dawei Cheng
In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions.
2 code implementations • 17 Aug 2023 • Linfeng Liu, Hongqiu Wu, Hai Zhao
However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error.
1 code implementation • 15 Aug 2023 • Qiwei Li, Zuchao Li, Xiantao Cai, Bo Du, Hai Zhao
In this paper, we propose GraphLayoutLM, a novel document understanding model that leverages the modeling of layout structure graph to inject document layout knowledge into the model.
1 code implementation • 2 Jul 2023 • Yineng Chen, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao
SGD and Adam are two classical and effective optimizers on which researchers have proposed many variants, such as SGDM and RAdam.
1 code implementation • 1 Jul 2023 • Zuchao Li, Shitou Zhang, Hai Zhao, Yifei Yang, Dongjie Yang
BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University.
1 code implementation • COLING 2022 • Jialin Chen, Zhuosheng Zhang, Hai Zhao
Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them.
1 code implementation • 19 Jun 2023 • Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao
To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework.
1 code implementation • 15 Jun 2023 • Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
1 code implementation • 28 May 2023 • Hongqiu Wu, Shaohua Zhang, Yuchen Zhang, Hai Zhao
In this paper, we study Chinese Spelling Correction (CSC) as a joint decision made by two separate models: a language model and an error model.
1 code implementation • 26 May 2023 • Yao Yao, Zuchao Li, Hai Zhao
Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph.
1 code implementation • 24 May 2023 • Dongjie Yang, Ruifeng Yuan, Yuantao Fan, Yifei Yang, Zili Wang, Shusen Wang, Hai Zhao
Therefore, we propose a method called RefGPT to generate enormous truthful and customized dialogues without worrying about factual errors caused by the model hallucination.
1 code implementation • 24 May 2023 • Yiyang Li, Xinting Huang, Wei Bi, Hai Zhao
Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows.
no code implementations • 23 May 2023 • Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan
Furthermore, to better align the query to the frozen modules, we propose a trainable scheme for our pipeline.
no code implementations • 22 May 2023 • Bohong Wu, Fei Yuan, Hai Zhao, Lei LI, Jingjing Xu
Considering that encoder-based models have the advantage of efficient generation and self-correction abilities, this paper explores methods to empower multilingual understanding models the generation abilities to get a unified model.
1 code implementation • 21 May 2023 • Yiyang Li, Hai Zhao
Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time.
1 code implementation • 11 May 2023 • Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, Nan Duan
Based on the remarkable achievements of pre-trained language models in abstractive summarization, the copying mechanism has proved helpful by improving the factuality, stability, and overall performance.
1 code implementation • 10 May 2023 • Anni Zou, Zhuosheng Zhang, Hai Zhao
Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not.
no code implementations • 9 May 2023 • Yifei Yang, Hongqiu Wu, Hai Zhao
This is due to the fine-grained nature of NER, as even minor word changes in the sentence can result in the emergence or mutation of any entities, resulting in invalid adversarial examples.
1 code implementation • 8 May 2023 • Hongqiu Wu, Yongxiang Liu, Hanwen Shi, Hai Zhao, Min Zhang
Based on the observation, we propose simple yet effective \textit{Contextualized representation-Adversarial Training} (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder.
3 code implementations • 2 Feb 2023 • Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola
Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer.
Ranked #3 on Science Question Answering on ScienceQA
no code implementations • 10 Jan 2023 • Zhuosheng Zhang, Hai Zhao, Longxiang Liu
We decouple the contextualized word representations by masking mechanisms in Transformer-based PrLM, making each word only focus on the words in current utterance, other utterances, and two speaker roles (i. e., utterances of sender and utterances of the receiver), respectively.
no code implementations • 9 Jan 2023 • Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao
Representation learning is the foundation of natural language processing (NLP).
no code implementations • 16 Dec 2022 • Junlong Li, Zhuosheng Zhang, Hai Zhao
Open-Domain Question Answering (ODQA) aims at answering factoid questions without explicitly providing specific background documents.
no code implementations • 1 Dec 2022 • Zhuosheng Zhang, Hai Zhao, Masao Utiyama, Eiichiro Sumita
Discriminative pre-trained language models (PLMs) learn to predict original texts from intentionally corrupted ones.
2 code implementations • 19 Oct 2022 • Hongqiu Wu, Ruixue Ding, Hai Zhao, Boli Chen, Pengjun Xie, Fei Huang, Min Zhang
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language models (PrLMs), generalizing well on a mass of scenarios.
1 code implementation • 16 Oct 2022 • Bohong Wu, Hai Zhao
Though offering amazing contextualized token-level representations, current pre-trained language models take less attention on accurately acquiring sentence-level representation during their self-supervised pre-training.
no code implementations • 13 Oct 2022 • Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao
In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base.
1 code implementation • 12 Oct 2022 • Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models.
Ranked #1 on Sentence Completion on HellaSwag
1 code implementation • 11 Oct 2022 • Zhuosheng Zhang, Hai Zhao, Ming Zhou
They treat training instances equally throughout the training process, with little attention on the individual contribution of those instances.
1 code implementation • COLING 2022 • Yiyang Li, Hongqiu Wu, Hai Zhao
Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks.
1 code implementation • 23 Aug 2022 • Dongjie Yang, Zhuosheng Zhang, Hai Zhao
Masked Language Modeling (MLM) has been widely used as the denoising objective in pre-training language models (PrLMs).
no code implementations • 23 Aug 2022 • Letian Peng, Zuchao Li, Hai Zhao
In detail, it works on PLMs according to the Replaced Token Detection (RTD) pre-training objective in ELECTRA, in which the corruption detection objective reflects the confidence on contextual integrity that is more relevant to commonsense reasoning than existing probability.
no code implementations • 21 Jul 2022 • Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
However, we find that most existing textual adversarial examples are unnatural, which can be easily distinguished by both human and machine.
1 code implementation • 25 Jun 2022 • Hongqiu Wu, Ruixue Ding, Hai Zhao, Pengjun Xie, Fei Huang, Min Zhang
Deep neural models (e. g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness.
Ranked #1 on Machine Reading Comprehension on DREAM
Machine Reading Comprehension Named Entity Recognition (NER) +4
1 code implementation • 30 Apr 2022 • Letian Peng, Zuchao Li, Hai Zhao
We report the performance of DeBERTaV3 on CommonsenseQA in this report.
no code implementations • 20 Apr 2022 • Bohong Wu, Hai Zhao
If self-supervised learning can be distinguished into two subcategories, generative and contrastive, then most existing studies show that sentence representation learning may more benefit from the contrastive methods but not the generative methods.
1 code implementation • 18 Apr 2022 • Yiyang Li, Hai Zhao, Zhuosheng Zhang
Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks.
no code implementations • 17 Apr 2022 • Yifei Yang, Zuchao Li, Hai Zhao
Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
1 code implementation • ACL 2022 • Yilin Zhao, Hai Zhao, Libin Shen, Yinggong Zhao
As a broad and major category in machine reading comprehension (MRC), the generalized goal of discriminative MRC is answer prediction from the given materials.
1 code implementation • Findings (ACL) 2022 • Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
We question the validity of current evaluation of robustness of PrLMs based on these non-natural adversarial samples and propose an anomaly detector to evaluate the robustness of PrLMs with more natural adversarial samples.
no code implementations • 4 Jan 2022 • Jiajia Li, Letian Peng, Ping Wang, Zuchao Li, Xueyi Li, Hai Zhao
As the model training on information from users is likely to invade personal privacy, many methods have been proposed to block the learning and memorizing of the sensitive data in raw texts.
1 code implementation • 26 Dec 2021 • Jiawei Wang, Hai Zhao
In detail, our model first focuses on key parts in the given context, and then generates highly related knowledge on such a basis in an association way like human thinking.
no code implementations • NeurIPS 2021 • Kailai Sun, Zuchao Li, Hai Zhao
The pre-trained language model (PrLM) demonstrates domination in downstream natural language processing tasks, in which multilingual PrLM takes advantage of language universality to alleviate the issue of limited resources for low-resource languages.
1 code implementation • EMNLP 2021 • Hongjiang Jing, Zuchao Li, Hai Zhao, Shu Jiang
Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • 29 Oct 2021 • Letian Peng, Zuchao Li, Hai Zhao
Unsupervised constituency parsing has been explored much but is still far from being solved.
1 code implementation • ACL 2022 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Tangled multi-party dialogue contexts lead to challenges for dialogue reading comprehension, where multiple dialogue threads flow simultaneously within a common dialogue record, increasing difficulties in understanding the dialogue history for both human and machine.
1 code implementation • ACL 2022 • Baorong Huang, Zhuosheng Zhang, Hai Zhao
In this paper, we imitate the human reading process in connecting the anaphoric expressions and explicitly leverage the coreference information of the entities to enhance the word embeddings from the pre-trained language model, in order to highlight the coreference mentions of the entities that must be identified for coreference-intensive question answering in QUOREF, a relatively new dataset that is specifically designed to evaluate the coreference-related performance of a model.
no code implementations • ACL 2022 • Bohong Wu, Zhuosheng Zhang, JinYuan Wang, Hai Zhao
In detail, we introduce an in-passage negative sampling strategy to encourage a diverse generation of sentence representations within the same passage.
no code implementations • 11 Oct 2021 • Zhuosheng Zhang, Hai Zhao
In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task.
1 code implementation • PACLIC 2021 • Yuchen He, Zhuosheng Zhang, Hai Zhao
Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC.
Ranked #1 on Discourse Parsing on Molweni
1 code implementation • 4 Oct 2021 • Letian Peng, Zuchao Li, Hai Zhao
Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation.
no code implementations • 29 Sep 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLMs) have been shown useful for enhancing a broad range of natural language understanding (NLU) tasks.
no code implementations • 14 Sep 2021 • Letian Peng, Zuchao Li, Hai Zhao
Attention scorers have achieved success in parsing tasks like semantic and syntactic dependency parsing.
no code implementations • 9 Sep 2021 • Xinbei Ma, Zhuosheng Zhang, Hai Zhao
Multi-party multi-turn dialogue comprehension brings unprecedented challenges on handling the complicated scenarios from multiple speakers and criss-crossed discourse relationship among speaker-aware utterances.
Ranked #1 on Question Answering on Molweni
1 code implementation • Findings (EMNLP) 2021 • Yiyang Li, Hai Zhao
Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue, resulting in intricate speaker information flows and noisy dialogue contexts.
Ranked #2 on Question Answering on FriendsQA
no code implementations • 31 Aug 2021 • Pengfei Zhu, Xiaoguang Li, Jian Li, Hai Zhao
Open-domain Question Answering (ODQA) has achieved significant results in terms of supervised learning manner.
Machine Reading Comprehension Open-Domain Question Answering
no code implementations • Findings (EMNLP) 2021 • Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words.
1 code implementation • EMNLP 2021 • Zhuosheng Zhang, Siru Ouyang, Hai Zhao, Masao Utiyama, Eiichiro Sumita
In this work, we propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation to provide a richer dialogue state reference.
no code implementations • 27 Jul 2021 • Zuchao Li, Kevin Parnow, Hai Zhao, Zhuosheng Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
no code implementations • 25 Jul 2021 • Bohong Wu, Zhuosheng Zhang, Hai Zhao
Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability.
1 code implementation • Findings (ACL) 2021 • Yi Xu, Hai Zhao
Pre-trained language models (PrLM) has been shown powerful in enhancing a broad range of downstream tasks including various dialogue related ones.
no code implementations • ACL 2021 • Yian Li, Hai Zhao
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific levels of linguistic units.
1 code implementation • 30 May 2021 • Rongzhou Bao, Jiayi Wang, Hai Zhao
In detail, we design an auxiliary anomaly detection classifier and adopt a multi-task learning procedure, by which PrLMs are able to distinguish adversarial input samples.
no code implementations • Findings (ACL) 2021 • Kevin Parnow, Zuchao Li, Hai Zhao
In Grammatical Error Correction (GEC), sequence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are passed to the model for multiple rounds of correction, which exposes the model to sentences with progressively fewer errors at each round.
no code implementations • ACL 2021 • Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLMs) have demonstrated superior performance due to their strong ability to learn universal language representations from self-supervised pre-training.
2 code implementations • NeurIPS 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Recent years have witnessed an increasing interest in training machines with reasoning ability, which deeply relies on accurately and clearly presented clue forms.
Ranked #23 on Reading Comprehension on ReClor
no code implementations • 20 May 2021 • Zuchao Li, Junru Zhou, Hai Zhao, Kevin Parnow
Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, Head-driven Phrase Structure Grammar (HPSG).
no code implementations • 19 Apr 2021 • Kashif Munir, Hai Zhao, Zuchao Li
To decompose the task as two argument related subtasks, identification and clustering, we propose a pipeline that correspondingly consists of two neural modules.
no code implementations • NeurIPS 2021 • Hongqiu Wu, Hai Zhao, Min Zhang
Beyond the success story of pre-trained language models (PrLMs) in recent natural language processing, they are susceptible to over-fitting due to unusual large model size.
no code implementations • EACL 2021 • Rui Wang, Hai Zhao
Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results.
no code implementations • 4 Mar 2021 • Zhuosheng Zhang, Hai Zhao
In this paper, we review the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task.
no code implementations • 11 Feb 2021 • Zuchao Li, Zhuosheng Zhang, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily.
no code implementations • 10 Feb 2021 • Zhuosheng Zhang, Junlong Li, Hai Zhao
Experimental results on four dialogue comprehension benchmark tasks show that our proposed model achieves great improvements on baselines.
no code implementations • 16 Jan 2021 • Sufeng Duan, Hai Zhao
We also propose a revisited multigraph called Multi-order-Graph (MoG) based on our explanation to model the graph structures in the SAN-based model as subgraphs in MoG and convert the encoding of SAN-based model to the generation of MoG.
no code implementations • 1 Jan 2021 • Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Instead of too early fixing the linguistic unit input as nearly all previous work did, we propose a novel method that combines span-level information into the representations generated by PrLMs during fine-tuning phase for better flexibility.
no code implementations • 1 Jan 2021 • Jeonghyeok Park, Hai Zhao
In this paper, we propose a novel method that infuses prior word alignment information into neural machine translation (NMT) to provide hints or guidelines for the target sentence at running time.
no code implementations • 1 Jan 2021 • Zuchao Li, Kevin Barry Parnow, Hai Zhao, Zhuosheng Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
no code implementations • 1 Jan 2021 • Fengshun Xiao, Zuchao Li, Hai Zhao
In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available.
no code implementations • 30 Dec 2020 • Rongzhou Bao, Jiayi Wang, Zhuosheng Zhang, Hai Zhao
By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity, and therefore to improve the understandability of sentences.
no code implementations • 30 Dec 2020 • Zhuosheng Zhang, Haojie Yu, Hai Zhao, Rui Wang, Masao Utiyama
Word representation is a fundamental component in neural language understanding models.
1 code implementation • Findings (ACL) 2021 • Hongqiu Wu, Hai Zhao, Min Zhang
Code summarization (CS) is becoming a promising area in recent language understanding, which aims to generate sensible human language automatically for programming language in the format of source code, serving in the most convenience of programmer developing.
1 code implementation • Findings (ACL) 2021 • Siru Ouyang, Zhuosheng Zhang, Hai Zhao
Conversational Machine Reading (CMR) aims at answering questions in a complicated manner.
no code implementations • 28 Dec 2020 • Yian Li, Hai Zhao
We present a universal representation model, BURT (BERT-inspired Universal Representation from learning meaningful segmenT), to encode different levels of linguistic unit into the same vector space.
no code implementations • 27 Dec 2020 • Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang
In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
no code implementations • 27 Dec 2020 • Kashif Munir, Hai Zhao, Zuchao Li
Semantic role labeling (SRL) aims at elaborating the meaning of a sentence by forming a predicate-argument structure.
no code implementations • 24 Dec 2020 • Kailai Sun, Zuchao Li, Hai Zhao
As it is unlikely to obtain a treebank for every human language, in this work, we propose an effective cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available.
1 code implementation • 7 Dec 2020 • Yilin Zhao, Zhuosheng Zhang, Hai Zhao
Thus we propose a novel reference-based knowledge enhancement model called Reference Knowledgeable Network (RekNet), which simulates human reading strategies to refine critical information from the passage and quote explicit knowledge in necessity.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Junru Zhou, Zhuosheng Zhang, Hai Zhao, Shuailiang Zhang
Besides, LIMIT-BERT takes a semi-supervised learning strategy to offer the same large amount of linguistics task data as that for the language model training.
no code implementations • 11 Oct 2020 • Zuchao Li, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Zuchao Li, Hai Zhao, Rui Wang, Kevin Parnow
Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships.
1 code implementation • 26 Sep 2020 • Yi Xu, Hai Zhao, Zhuosheng Zhang
In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances.
no code implementations • 16 Sep 2020 • Shu Jiang, Hai Zhao, Zuchao Li, Bao-liang Lu
Standard neural machine translation (NMT) is on the assumption of document-level context independent.
no code implementations • 16 Sep 2020 • Sufeng Duan, Hai Zhao, Rui Wang
In the light of the current NMT models more or less capture graph information among the sequence in a latent way, we present a graph-to-sequence model facilitating explicit graph information capturing.
no code implementations • 15 Sep 2020 • Junjie Yang, Zhuosheng Zhang, Hai Zhao
Generative machine reading comprehension (MRC) requires a model to generate well-formed answers.
1 code implementation • 14 Sep 2020 • Longxiang Liu, Zhuosheng Zhang, Hai Zhao, Xi Zhou, Xiang Zhou
A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles.
no code implementations • 14 Sep 2020 • Zhuosheng Zhang, Yiqing Zhang, Hai Zhao, Xi Zhou, Xiang Zhou
This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages.
no code implementations • CL (ACL) 2021 • Zuchao Li, Hai Zhao, Shexia He, Jiaxun Cai
Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence.
no code implementations • 10 Sep 2020 • Yian Li, Hai Zhao
Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way.
1 code implementation • 10 Sep 2020 • Junlong Li, Zhuosheng Zhang, Hai Zhao
Pre-trained language models (PrLMs) have achieved great success on a wide range of natural language processing tasks by virtue of the universal language representation ability obtained by self-supervised learning on a large corpus.
1 code implementation • 13 May 2020 • Zhuosheng Zhang, Hai Zhao, Rui Wang
In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches.
no code implementations • ICLR 2020 • Zuchao Li, Rui Wang, Kehai Chen, Masso Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
1 code implementation • ACL 2020 • Ying Luo, Hai Zhao
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER), which contains two subgraph modules: a flat NER module for outermost entities and a graph module for all the entities located in inner layers.
Ranked #6 on Nested Mention Recognition on ACE 2005
1 code implementation • ICLR 2020 • Zhuosheng Zhang, Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao
Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations.
no code implementations • 30 Apr 2020 • Sufeng Duan, Juncheng Cao, Hai Zhao
In this paper, we thus propose the capsule-Transformer, which extends the linear transformation into a more general capsule routing algorithm by taking SAN as a special case of capsule network.
no code implementations • 29 Apr 2020 • Junlong Li, Zhuosheng Zhang, Hai Zhao
In this paper, the relevance of each turn to the question are calculated to choose key turns.
no code implementations • 29 Apr 2020 • Sufeng Duan, Hai Zhao, Dong-dong Zhang, Rui Wang
Data augmentation is an effective performance enhancement in neural machine translation (NMT) by generating additional bilingual data.
no code implementations • 29 Apr 2020 • Yian Li, Hai Zhao
Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream Natural Language Processing (NLP) tasks.
no code implementations • 28 Apr 2020 • Shuailiang Zhang, Hai Zhao, Junru Zhou
Taking explicit contextualized semantics as a complementary input, the inferential module of SAIN enables a series of reasoning steps over semantic clues through an attention mechanism.
no code implementations • NAACL 2021 • Mingxuan Wang, Hongxiao Bai, Hai Zhao, Lei LI
Neural machine translation~(NMT) is ineffective for zero-resource languages.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Zuchao Li, Hai Zhao, Rui Wang, Masao Utiyama, Eiichiro Sumita
Further enriching the idea of pivot translation by extending the use of parallel corpora beyond the source-target paradigm, we propose a new reference language-based framework for UNMT, RUNMT, in which the reference language only shares a parallel corpus with the source, but this corpus still indicates a signal clear enough to help the reconstruction training of UNMT through a proposed reference agreement mechanism.
2 code implementations • 27 Jan 2020 • Zhuosheng Zhang, Junjie Yang, Hai Zhao
Inspired by how humans solve reading comprehension questions, we proposed a retrospective reader (Retro-Reader) that integrates two stages of reading and verification strategies: 1) sketchy reading that briefly investigates the overall interactions of passage and question, and yield an initial judgment; 2) intensive reading that verifies the answer and gives the final prediction.
Ranked #7 on Question Answering on SQuAD2.0
3 code implementations • 26 Jan 2020 • Pengfei Zhu, Hai Zhao, Xiaoguang Li
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
Ranked #3 on Reading Comprehension on RACE
no code implementations • 1 Jan 2020 • Pengfei Zhu, Hai Zhao, Xiaoguang Li
Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question.
1 code implementation • 27 Dec 2019 • Zuchao Li, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Zhuosheng Zhang, Hai Zhao
In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT.
2 code implementations • 25 Nov 2019 • Jeonghyeok Park, Hai Zhao
Korean-Chinese is a low resource language pair, but Korean and Chinese have a lot in common in terms of vocabulary.
1 code implementation • 20 Nov 2019 • Zuchao Li, Hai Zhao, Kevin Parnow
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models.
no code implementations • 7 Nov 2019 • Zhuosheng Zhang, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita, Hai Zhao
We present a universal framework to model contextualized sentence representations with visual awareness that is motivated to overcome the shortcomings of the multimodal parallel data with manual annotations.
no code implementations • 7 Nov 2019 • Zuchao Li, Hai Zhao, Junru Zhou, Kevin Parnow, Shexia He
In this paper, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role, providing a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation.
1 code implementation • 6 Nov 2019 • Ying Luo, Fengshun Xiao, Hai Zhao
In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation.
Ranked #13 on Named Entity Recognition (NER) on Ontonotes v5 (English) (using extra training data)
no code implementations • 5 Nov 2019 • Junjie Yang, Hai Zhao
Transformer-based pre-trained language models have proven to be effective for learning contextualized language representation.
no code implementations • CONLL 2019 • Zuchao Li, Hai Zhao, Zhuosheng Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita
This paper describes our SJTU-NICT{'}s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
no code implementations • CONLL 2019 • Hongxiao Bai, Hai Zhao
This paper describes the system of our team SJTU for our participation in the CoNLL 2019 Shared Task: Cross-Framework Meaning Representation Parsing.
1 code implementation • EMNLP 2020 • Sufeng Duan, Hai Zhao
Taking greedy decoding algorithm as it should be, this work focuses on further strengthening the model itself for Chinese word segmentation (CWS), which results in an even more fast and more accurate CWS model.
no code implementations • 31 Oct 2019 • Shu Jiang, Rui Wang, Zuchao Li, Masao Utiyama, Kehai Chen, Eiichiro Sumita, Hai Zhao, Bao-liang Lu
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
no code implementations • 31 Oct 2019 • Junru Zhou, Zhuosheng Zhang, Hai Zhao, Shuailiang Zhang
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL).
no code implementations • 18 Sep 2019 • Jiangtong Li, Hai Zhao, Zuchao Li, Wei Bi, Xiaojiang Liu
Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models. However, the character is an insufficient and unnatural linguistic unit for word representation. Thus we introduce Embedding from Subword-aware Language Models (ESuLMo) which learns word representation from subwords using unsupervised segmentation over words. We show that ESuLMo can enhance four benchmark NLP tasks more effectively than ELMo, including syntactic dependency parsing, semantic role labeling, implicit discourse relation recognition and textual entailment, which brings a meaningful improvement over ELMo.
1 code implementation • 5 Sep 2019 • Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks.
Ranked #6 on Natural Language Inference on SNLI
no code implementations • 3 Sep 2019 • Zhuosheng Zhang, Zhen Meng, Hai Zhao
This paper presents a smart sliding Chinese pinyin Input Method Editor (IME) for touchscreen devices which allows user finger sliding from one key to another on the touchscreen instead of tapping keys one by one, while the target Chinese character sequence will be predicted during the sliding process to help user input Chinese characters efficiently.
no code implementations • 3 Sep 2019 • Zhuosheng Zhang, Bingjie Tang, Zuchao Li, Hai Zhao
This work models named entity distribution from a way of visualizing topological structure of embedding space, so that we make an assumption that most, if not all, named entities (NEs) for a language tend to aggregate together to be accommodated by a specific hypersphere in embedding space.
1 code implementation • IJCNLP 2019 • Shexia He, Zuchao Li, Hai Zhao
Recently, semantic role labeling (SRL) has earned a series of success with even higher performance improvements, which can be mainly attributed to syntactic integration and enhanced word representation.
no code implementations • EMNLP 2020 • Ying Luo, Hai Zhao, Junlang Zhan
Deep neural network models have helped named entity (NE) recognition achieve amazing performance without handcrafting features.
no code implementations • 31 Aug 2019 • Ying Luo, Hai Zhao, Zhuosheng Zhang, Bingjie Tang
For monolingual cases, the proposed named entity model gives an open description of diverse named entity types and different languages.
2 code implementations • 30 Aug 2019 • Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Junru Zhou, Zuchao Li, Hai Zhao
Both syntactic and semantic structures are key linguistic contextual clues, in which parsing the latter has been well shown beneficial from parsing the former.
no code implementations • 29 Aug 2019 • Hongxiao Bai, Hai Zhao, Junhan Zhao
As implicit discourse relation recognizer has to carefully tackle the semantic similarity of the given sentence pairs and the severe data sparsity issue exists in the meantime, it is supposed to be beneficial from mastering the entire training data.
no code implementations • 22 Aug 2019 • Zuchao Li, Hai Zhao, Yingting Wu, Fengshun Xiao, Shu Jiang
Our experiments indicate that switching to the DSD loss after the convergence of ML training helps models escape local optima and stimulates stable performance improvements.
no code implementations • 18 Aug 2019 • Junru Zhou, Shuailiang Zhang, Hai Zhao
Constituent and dependency representation for syntactic structure share a lot of linguistic and computational characteristics, this paper thus makes the first attempt by introducing a new model that is capable of parsing constituent and dependency at the same time, so that lets either of the parsers enhance each other.
1 code implementation • 14 Aug 2019 • Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang
In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Ranked #5 on Question Answering on SQuAD2.0 dev
1 code implementation • NAACL 2019 • Chaoyu Guan, Yuhao Cheng, Hai Zhao
Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented.
1 code implementation • ACL 2019 • Junru Zhou, Hai Zhao
In details, we report 96. 33 F1 of constituent parsing and 97. 20\% UAS of dependency parsing on PTB.
Ranked #4 on Constituency Parsing on Penn Treebank
no code implementations • ACL 2019 • Fengshun Xiao, Jiangtong Li, Hai Zhao, Rui Wang, Kehai Chen
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
no code implementations • NAACL 2019 • Pengshuai Li, Xinsong Zhang, Weijia Jia, Hai Zhao
Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently.
no code implementations • ICLR 2019 • Huan Zhang, Hai Zhao
Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction.
no code implementations • 22 Apr 2019 • Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-liang Lu, Ning Xia
Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference.
1 code implementation • 30 Jan 2019 • Junlang Zhan, Hai Zhao
Open information extraction (Open IE) is a challenging task especially due to its brittle data basis.
Ranked #5 on Open Information Extraction on OIE2016
no code implementations • 27 Jan 2019 • Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Ranked #3 on Question Answering on RACE
1 code implementation • ICLR 2019 • Junlang Zhan, Hai Zhao
Chemical information extraction is to convert chemical knowledge in text into true chemical database, which is a text processing task heavily relying on chemical compound name identification and standardization.
no code implementations • 18 Jan 2019 • Hai Zhao, Deng Cai, Changning Huang, Chunyu Kit
This paper reviews the development of Chinese word segmentation (CWS) in the most recent decade, 2007-2017.
1 code implementation • 16 Jan 2019 • Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence.
Ranked #9 on Semantic Role Labeling on CoNLL 2005
no code implementations • 11 Nov 2018 • Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao
To disclose overlapped multiple relations from a sentence still keeps challenging.
1 code implementation • ACL 2019 • Zhuosheng Zhang, Yafang Huang, Hai Zhao
Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME).
1 code implementation • 8 Nov 2018 • Zuchao Li, Jiaxun Cai, Hai Zhao
Easy-first parsing relies on subtree re-ranking to build the complete parse tree.
1 code implementation • 6 Nov 2018 • Zhuosheng Zhang, Hai Zhao, Kangwei Ling, Jiangtong Li, Zuchao Li, Shexia He, Guohong Fu
Representation learning is the foundation of machine reading comprehension and inference.
no code implementations • 6 Nov 2018 • Sufeng Duan, Jiangtong Li, Hai Zhao
Rapidly developed neural models have achieved competitive performance in Chinese word segmentation (CWS) as their traditional counterparts.
1 code implementation • EMNLP 2018 • Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, Luo Si
Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence.
no code implementations • CONLL 2018 • Yingting Wu, Hai Zhao, Jia-Jun Tong
This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
1 code implementation • CONLL 2018 • Zuchao Li, Shexia He, Zhuosheng Zhang, Hai Zhao
This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
no code implementations • 8 Sep 2018 • Zhuosheng Zhang, Yuwei Wu, Zuchao Li, Hai Zhao
Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task.
Ranked #8 on Natural Language Inference on SNLI
Machine Reading Comprehension Natural Language Understanding +1
no code implementations • 8 Sep 2018 • Zhuosheng Zhang, Shexia He, Zuchao Li, Hai Zhao
The goal of semantic role labeling (SRL) is to discover the predicate-argument structure of a sentence, which plays a critical role in deep processing of natural language.
1 code implementation • EMNLP 2018 • Yafang Huang, Hai Zhao
Chinese pinyin input method engine (IME) converts pinyin into character so that Chinese characters can be conveniently inputted into computer through common keyboard.
1 code implementation • EMNLP 2018 • Zhisong Zhang, Rui Wang, Masao Utiyama, Eiichiro Sumita, Hai Zhao
In Neural Machine Translation (NMT), the decoder can capture the features of the entire prediction history with neural connections and representations.
1 code implementation • 11 Aug 2018 • Jiaxun Cai, Shexia He, Zuchao Li, Hai Zhao
Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling.
no code implementations • COLING 2018 • Pengfei Zhu, Zhuosheng Zhang, Jiangtong Li, Yafang Huang, Hai Zhao
Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method.
no code implementations • 7 Aug 2018 • Zhuosheng Zhang, Yafang Huang, Pengfei Zhu, Hai Zhao
Machine reading comprehension is a task to model relationship between passage and query.
3 code implementations • COLING 2018 • Zuchao Li, Jiaxun Cai, Shexia He, Hai Zhao
This paper presents a sequence to sequence (seq2seq) dependency parser by directly predicting the relative position of head for each given word, which therefore results in a truly end-to-end seq2seq dependency parser for the first time.
no code implementations • COLING 2018 • Jiaxun Cai, Shexia He, Zuchao Li, Hai Zhao
Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling.