Search Results for author:
Found 84 papers, 43 papers with code
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging
Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain.
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study
Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest.
RST Discourse Parsing with Second-Stage EDU-Level Pre-training
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.
APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing
Thanks to the strong representation learning capability of deep learning, especially pre-training techniques with language model loss, dependency parsing has achieved great performance boost in the in-domain scenario with abundant labeled training data for target domains.
Cross-Lingual Dependency Parsing via Self-Training
Recent advances of multilingual word representations weaken the input divergences across languages, making cross-lingual transfer similar to the monolingual cross-domain and semi-supervised settings.
Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation Benchmark
T3DEM is the most crucial step in determining the quality of Emo3D generation and encompasses three key challenges: Expression Diversity, Emotion-Content Consistency, and Expression Fluidity.
Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image
In the visual spatial understanding (VSU) area, spatial image-to-text (SI2T) and spatial text-to-image (ST2I) are two fundamental tasks that appear in dual form.
Grammar Induction from Visual, Speech and Text
Grammar Induction could benefit from rich heterogeneous signals, such as text, vision, and acoustics.
An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
We propose the first end-to-end model for photo-sharing multi-modal dialogue generation, which integrates an image perceptron and an image generator with a large language model.
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders).
Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment
Then, an SG-based framework is built, where the textual SG (TSG) is encoded with a graph Transformer, while the video dynamic SG (DSG) and the HSG are modeled with a novel recurrent graph Transformer for spatial and temporal feature propagation.
LLM-Driven Multimodal Opinion Expression Identification
We introduce a novel multimodal OEI (MOEI) task, integrating text and speech to mirror real-world scenarios.
Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification
Hierarchical text classification (HTC) is an important task with broad applications, while few-shot HTC has gained increasing interest recently.
AutoSurvey: Large Language Models Can Automatically Write Surveys
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence.
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Experimental results on Chinese sequence labeling datasets demonstrate that the improved BABERT variant outperforms the vanilla version, not only on these tasks but also more broadly across a range of Chinese natural language understanding tasks.
Cross-domain Chinese Sentence Pattern Parsing
Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching. Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability. To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework.
In-Context Learning for Few-Shot Nested Named Entity Recognition
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address.
A Two-Stage Adaptation of Large Language Models for Text Ranking
In this work, we propose a two-stage progressive paradigm to better adapt LLMs to text ranking.
LLM-enhanced Self-training for Cross-domain Constituency Parsing
Our experimental results demonstrate that self-training for constituency parsing, equipped with an LLM, outperforms traditional methods regardless of the LLM's performance.
Language Models are Universal Embedders
As such cases span from English to other natural or programming languages, from retrieval to classification and beyond, it is desirable to build a unified embedding model rather than dedicated ones for each scenario.
Grounded Entity-Landmark Adaptive Pre-training for Vision-and-Language Navigation
To address this problem, we propose a novel Grounded Entity-Landmark Adaptive (GELA) pre-training paradigm for VLN tasks.
Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling
A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.
Towards General Text Embeddings with Multi-stage Contrastive Learning
We present GTE, a general-purpose text embedding model trained with multi-stage contrastive learning.
XNLP: An Interactive Demonstration System for Universal Structured NLP
Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications.
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing.
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese
Dialogue-level dependency parsing has received insufficient attention, especially for Chinese.
Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination
In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.
Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction
Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e. g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages.
Generating Visual Spatial Description via Holistic 3D Scene Understanding
With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
Improving Simultaneous Machine Translation with Monolingual Data
Preliminary experiments on En-Zh and En-Ja news domain corpora demonstrate that monolingual data can significantly improve translation quality (e. g., +3. 15 BLEU on En-Zh).
Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
We apply BABERT for feature induction of Chinese sequence labeling tasks.
Ranked #1 on
Chinese Word Segmentation
on MSRA
Chinese Named Entity Recognition
Chinese Word Segmentation
+3
Extending Phrase Grounding with Pronouns in Visual Dialogues
First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions.
Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation
Accordingly, we manually annotate a dataset to facilitate the investigation of the newly-introduced task and build several benchmark encoder-decoder models by using VL-BART and VL-T5 as backbones.
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph
In this work, we investigate the integration of a latent graph for CSRL.
Domain-Specific NER via Retrieving Correlated Samples
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Word ordering is a constrained language generation task taking unordered words as input.
AISHELL-NER: Named Entity Recognition from Chinese Speech
Named Entity Recognition (NER) from speech is among Spoken Language Understanding (SLU) tasks, aiming to extract semantic information from the speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
Unified Named Entity Recognition as Word-Word Relation Classification
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka.
Ranked #2 on
Chinese Named Entity Recognition
on OntoNotes 4
Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.
Ranked #1 on
Fine-Grained Opinion Analysis
on MPQA
(F1 (Opinion) metric)
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem.
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession.
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.
End-to-end Semantic Role Labeling with Neural Transition-based Model
It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly.
Ranked #1 on
Semantic Role Labeling
on CoNLL-2009
HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations
Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers.
Ranked #21 on
Emotion Recognition in Conversation
on EmoryNLP
Cross-lingual Semantic Role Labeling with Model Transfer
In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods.
A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures
This article briefly reviews the representative models of constituent parsing and dependency parsing, and also dependency graph parsing with rich semantics.
DRTS Parsing with Structure-Aware Encoding and Decoding
Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently.
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding.
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition.
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community.
Ranked #24 on
Emotion Recognition in Conversation
on EmoryNLP
Syntax-aware Neural Semantic Role Labeling
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling
Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger.
Ranked #1 on
Fine-Grained Opinion Analysis
on MPQA
(using extra training data)
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Syntax has been demonstrated highly effective in neural machine translation (NMT).
Ranked #8 on
Machine Translation
on IWSLT2015 English-Vietnamese
Transition-based Neural RST Parsing with Implicit Syntax Features
Syntax has been a useful source of information for statistical RST discourse parsing.
Ranked #3 on
Discourse Parsing
on RST-DT
(RST-Parseval (Full) metric)
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
Transition-Based Disfluency Detection using LSTMs
In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information.
End-to-End Neural Relation Extraction with Global Optimization
Neural networks have shown promising results for relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Combining Discrete and Neural Features for Sequence Labeling
Our results on standard benchmarks show that state-of-the-art neural models can give accuracies comparable to the best discrete models in the literature for most tasks and combing discrete and neural features unanimously yield better results.
Recognizing irregular entities in biomedical text via deep neural networks
We evaluate our models on two datasets for recognizing regular and irreg- ular biomedical entities.
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems.
Tweet Sarcasm Detection Using Deep Neural Network
We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
This model divides a sentence or text segment into five parts, namely two target entities and their three contexts.
LibN3L:A Lightweight Package for Neural NLP
We present a light-weight machine learning tool for NLP research.
