Search Results for author:
Found 97 papers, 16 papers with code
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence
In this paper, we focus on multi-label emotion detection in a multi-modal scenario.
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation
Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora.
EDTC: A Corpus for Discourse-Level Topic Chain Parsing
Discourse analysis has long been known to be fundamental in natural language processing.
CVAE-based Re-anchoring for Implicit Discourse Relation Classification
Training implicit discourse relation classifiers suffers from data sparsity.
基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints)
现有的对话系统中存在着生成“好的”、“我不知道”等无意义的安全回复问题。日常对话中, 对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型, 即在Seq2Seq模型的基础上, 结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束, 从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明, 该文提出的方法能有效地提高生成回复的质量。
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection
Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).
Feature-level Incongruence Reduction for Multimodal Translation
Besides of linguistic features in captions, MNMT allows visual(image) features to be used.
Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection
The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process.
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning
Document-level Event Factuality Identification (DEFI) predicts the factuality of a specific event based on a document from which the event can be derived, which is a fundamental and crucial task in Natural Language Processing (NLP).
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment Classification
Knowledge distillation is an effective method to transfer knowledge from a large pre-trained teacher model to a compacted student model.
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation
The former aims to adapt to characteristics of low-resource languages during encoding, while the latter adapts to translation experiences learned from high-resource languages.
Language Modelling
Low-Resource Neural Machine Translation
+5
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
Opinion Tree Parsing for Aspect-based Sentiment Analysis
To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure.
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent.
Joint Learning-based Causal Relation Extraction from Biomedical Literature
Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions.
Pre-trained Token-replaced Detection Model as Few-shot Learner
In this paper, as an alternative, we propose a novel approach to few-shot learning with pre-trained token-replaced detection models like ELECTRA.
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation
We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages.
Adversarial Learning for Discourse Rhetorical Structure Parsing
Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data.
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training
To this end, we propose two pre-training tasks.
More than Text: Multi-modal Chinese Word Segmentation
Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing.
Coreference Resolution: Are the eliminated spans totally worthless?
Various neural-based methods have been proposed so far for joint mention detection and coreference resolution.
Sentiment Forecasting in Dialog
Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation.
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.
Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
We tackle implicit discourse relation recognition.
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection
To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i. e., texts and images) for depression detection.
Improving AMR Parsing with Sequence-to-Sequence Pre-training
In the literature, the research on abstract meaning representation (AMR) parsing is much restricted by the size of human-curated dataset which is critical to build an AMR parser with good performance.
Ranked #13 on
AMR Parsing
on LDC2017T10
(using extra training data)
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling
This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.
A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years.
A Discrete CVAE for Response Generation on Short-Text Conversation
In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation
In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT).
Negative Focus Detection via Contextual Attention Mechanism
For the sake of understanding a negated statement, it is critical to precisely detect the negative focus in context.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence.
Emotion Detection with Neural Personal Discrimination
Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations
On the one hand, our approach represents each utterance and each speaker as a node.
Ranked #46 on
Emotion Recognition in Conversation
on MELD
Adversarial Attention Modeling for Multi-dimensional Emotion Regression
In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics.
Document-Level Event Factuality Identification via Adversarial Neural Network
Document-level event factuality identification is an important subtask in event factuality and is crucial for discourse understanding in Natural Language Processing (NLP).
Using active learning to expand training data for implicit discourse relation recognition
We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network
On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair.
Stance Detection with Hierarchical Attention Network
In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information.
MCDTB: A Macro-level Chinese Discourse TreeBank
In view of the differences between the annotations of micro and macro discourse rela-tionships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus.
One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community.
Adversarial Feature Adaptation for Cross-lingual Relation Classification
In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events.
Cross-media User Profiling with Joint Textual and Social User Embedding
In realistic scenarios, a user profiling model (e. g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media.
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse
The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information.
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection
Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent.
Aspect Sentiment Classification with both Word-level and Clause-level AttentionNetworks
Aspect sentiment classification, a challenging taskin sentiment analysis, has been attracting more andmore attention in recent years.
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text.
Modeling Source Syntax for Neural Machine Translation
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements.
A Bilingual Attention Network for Code-switched Emotion Prediction
Visualization of the attention layers illustrates that the model selects qualitatively informative words.
Two-View Label Propagation to Semi-supervised Reader Emotion Classification
In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm.
User Classification with Multiple Textual Perspectives
Textual information is of critical importance for automatic user classification in social media.
Global Inference to Chinese Temporal Relation Extraction
Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences.
Semi-supervised Gender Classification with Joint Textual and Social Modeling
In gender classification, labeled data is often limited while unlabeled data is ample.
Corpus Fusion for Emotion Classification
Machine learning-based methods have obtained great progress on emotion classification.
