Search Results for author: Xinyan Xiao

Found 42 papers, 14 papers with code

Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification

no code implementations EMNLP 2020 Yunjie Ji, Hao liu, Bolei He, Xinyan Xiao, Hua Wu, Yanhua Yu

To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.

General Classification Multiple Instance Learning +2

SgSum:Transforming Multi-document Summarization into Sub-graph Selection

1 code implementation EMNLP 2021 Moye Chen, Wei Li, Jiachen Liu, Xinyan Xiao, Hua Wu, Haifeng Wang

Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.

Document Summarization Multi-Document Summarization

UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning

no code implementations23 May 2023 Hao Yang, Can Gao, Hao Líu, Xinyan Xiao, Yanyan Zhao, Bing Qin

The experimental results show that our model achieves state-of-the-art performance in various downstream tasks, and through ablation study can prove that effective cross-layer learning improves the model's ability of multimodal representation.

Representation Learning

WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning

1 code implementation20 Dec 2022 Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Sujian Li, Yajuan Lv

As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks.

Natural Language Inference Question Answering +2

FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness

no code implementations1 Nov 2022 Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Ziqiang Cao, Sujian Li, Hua Wu

We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.

Abstractive Text Summarization

Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation

no code implementations22 Oct 2022 Wenhao Wu, Wei Li, Jiachen Liu, Xinyan Xiao, Sujian Li, Yajuan Lyu

Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.

Informativeness Text Generation

SeSQL: Yet Another Large-scale Session-level Chinese Text-to-SQL Dataset

no code implementations26 Aug 2022 Saihao Huang, Lijie Wang, Zhenghua Li, Zeyang Liu, Chenhui Dou, Fukang Yan, Xinyan Xiao, Hua Wu, Min Zhang

As the first session-level Chinese dataset, CHASE contains two separate parts, i. e., 2, 003 sessions manually constructed from scratch (CHASE-C), and 3, 456 sessions translated from English SParC (CHASE-T).

SQL Parsing Text-To-SQL

An Interpretability Evaluation Benchmark for Pre-trained Language Models

no code implementations28 Jul 2022 Yaozong Shen, Lijie Wang, Ying Chen, Xinyan Xiao, Jing Liu, Hua Wu

To fill in the gap, we propose a novel evaluation benchmark providing with both English and Chinese annotated data.

A Fine-grained Interpretability Evaluation Benchmark for Neural NLP

no code implementations23 May 2022 Lijie Wang, Yaozong Shen, Shuyuan Peng, Shuai Zhang, Xinyan Xiao, Hao liu, Hongxuan Tang, Ying Chen, Hua Wu, Haifeng Wang

Based on this benchmark, we conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.

Reading Comprehension Sentiment Analysis

Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss

no code implementations26 Apr 2022 Kun Wu, Lijie Wang, Zhenghua Li, Xinyan Xiao

Grammar-based parsers have achieved high performance in the cross-domain text-to-SQL parsing task, but suffer from low decoding efficiency due to the much larger number of actions for grammar selection than that of tokens in SQL queries.

SQL Parsing Text-To-SQL

UNIMO-2: End-to-End Unified Vision-Language Grounded Learning

1 code implementation Findings (ACL) 2022 Wei Li, Can Gao, guocheng niu, Xinyan Xiao, Hao liu, Jiachen Liu, Hua Wu, Haifeng Wang

In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.

PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation

no code implementations ACL 2022 Zhe Hu, Hou Pong Chan, Jiachen Liu, Xinyan Xiao, Hua Wu, Lifu Huang

Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.

Contrastive Learning Text Generation

Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods

no code implementations10 Mar 2022 Wei Li, Wenhao Wu, Moye Chen, Jiachen Liu, Xinyan Xiao, Hua Wu

In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods.

Abstractive Text Summarization Data-to-Text Generation +2

Learning with Noisy Correspondence for Cross-modal Matching

1 code implementation NeurIPS 2021 Zhenyu Huang, guocheng niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng

Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.

Image-text matching Memorization +2

SgSum: Transforming Multi-document Summarization into Sub-graph Selection

1 code implementation25 Oct 2021 Moye Chen, Wei Li, Jiachen Liu, Xinyan Xiao, Hua Wu, Haifeng Wang

Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.

Document Summarization Multi-Document Summarization

A Multimodal Sentiment Dataset for Video Recommendation

no code implementations17 Sep 2021 Hongxuan Tang, Hao liu, Xinyan Xiao, Hua Wu

Based on this, we propose a multimodal sentiment analysis dataset, named baiDu Video Sentiment dataset (DuVideoSenti), and introduce a new sentiment system which is designed to describe the sentimental style of a video on recommendation scenery.

Multimodal Sentiment Analysis Video Understanding

Controllable Dialogue Generation with Disentangled Multi-grained Style Specification and Attribute Consistency Reward

no code implementations14 Sep 2021 Zhe Hu, Zhiwei Cao, Hou Pong Chan, Jiachen Liu, Xinyan Xiao, Jinsong Su, Hua Wu

Controllable text generation is an appealing but challenging task, which allows users to specify particular attributes of the generated outputs.

Dialogue Generation Response Generation

Fine-grained Entity Typing via Label Reasoning

no code implementations EMNLP 2021 Qing Liu, Hongyu Lin, Xinyan Xiao, Xianpei Han, Le Sun, Hua Wu

Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.

Entity Typing

DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation

no code implementations30 Aug 2021 Lijie Wang, Hao liu, Shuyuan Peng, Hongxuan Tang, Xinyan Xiao, Ying Chen, Hua Wu, Haifeng Wang

Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.

Sentiment Analysis

BASS: Boosting Abstractive Summarization with Unified Semantic Graph

no code implementations ACL 2021 Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.

Abstractive Text Summarization Document Summarization +2

A Practical Chinese Dependency Parser Based on A Large-scale Dataset

2 code implementations2 Sep 2020 Shuai Zhang, Lijie Wang, Ke Sun, Xinyan Xiao

DDParser is extended on the graph-based biaffine parser to accommodate to the characteristics of Chinese dataset.

Dependency Parsing Test

Leveraging Graph to Improve Abstractive Multi-Document Summarization

2 code implementations ACL 2020 Wei Li, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, Junping Du

Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries.

Document Summarization Multi-Document Summarization

SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis

6 code implementations ACL 2020 Hao Tian, Can Gao, Xinyan Xiao, Hao liu, Bolei He, Hua Wu, Haifeng Wang, Feng Wu

In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.

Multi-Label Classification Sentiment Analysis

Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer

1 code implementation ACL 2020 Chulun Zhou, Liang-Yu Chen, Jiachen Liu, Xinyan Xiao, Jinsong Su, Sheng Guo, Hua Wu

Unsupervised style transfer aims to change the style of an input sentence while preserving its original content without using parallel training data.

Denoising Style Transfer

ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification

no code implementations ACL 2019 Wei Jia, Dai Dai, Xinyan Xiao, Hua Wu

In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification.

Classification General Classification +1

Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension

no code implementations ACL 2018 Zhen Wang, Jiachen Liu, Xinyan Xiao, Yajuan Lyu, Tian Wu

While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.

Answer Selection Reading Comprehension

DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications

3 code implementations WS 2018 Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yu-An Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang

Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.

Machine Reading Comprehension

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