Search Results for author: Baobao Chang

Found 66 papers, 15 papers with code

Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification

no code implementations29 Aug 2021 Peiyi Wang, Runxin Xu, Tianyu Liu, Damai Dai, Baobao Chang, Zhifang Sui

However, we find they suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types, which we summarize as trigger overlapping and trigger separability.

Explicit Interaction Network for Aspect Sentiment Triplet Extraction

no code implementations21 Jun 2021 Peiyi Wang, Lianzhe Huang, Tianyu Liu, Damai Dai, Runxin Xu, Houfeng Wang, Baobao Chang, Zhifang Sui

In this paper, we divide ASTE into target-opinion joint detection and sentiment classification subtasks, which is in line with human cognition, and correspondingly propose sequence encoder and table encoder.

Aspect Sentiment Triplet Extraction

SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction

1 code implementation3 Jun 2021 Shuang Zeng, Yuting Wu, Baobao Chang

However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph.

Document-level Relation Extraction

Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation

no code implementations NAACL 2021 Hua Zheng, Damai Dai, Lei LI, Tianyu Liu, Zhifang Sui, Baobao Chang, Yang Liu

In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word.

Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker

1 code implementation ACL 2021 Runxin Xu, Tianyu Liu, Lei LI, Baobao Chang

Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model.

Document-level Document-level Event Extraction +1

Problems and Countermeasures in Natural Language Processing Evaluation

no code implementations20 Apr 2021 Qingxiu Dong, Zhifang Sui, Weidong Zhan, Baobao Chang

Starting from the concept, com-position, development and meaning of natural language evaluation, this article classifies and summarizes the tasks and char-acteristics of mainstream natural language evaluation, and then summarizes the problems and causes of natural language pro-cessing evaluation.

Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension

no code implementations26 Mar 2021 Damai Dai, Hua Zheng, Zhifang Sui, Baobao Chang

Conventional Machine Reading Comprehension (MRC) has been well-addressed by pattern matching, but the ability of commonsense reasoning remains a gap between humans and machines.

Knowledge Graph Embeddings Knowledge Graphs +1

Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

1 code implementation17 Feb 2021 Tianyu Liu, Xin Zheng, Baobao Chang, Zhifang Sui

In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record.

Few-Shot Learning Table-to-Text Generation

Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement

no code implementations14 Dec 2020 Tianyang Cao, Shuang Zeng, Songge Zhao, Mairgup Mansur, Baobao Chang

Recent years have seen significant advancement in text generation tasks with the help of neural language models.

Text Generation

Coarse-to-Fine Entity Representations for Document-level Relation Extraction

1 code implementation4 Dec 2020 Damai Dai, Jing Ren, Shuang Zeng, Baobao Chang, Zhifang Sui

In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction.

Document-level Relation Extraction

An Anchor-Based Automatic Evaluation Metric for Document Summarization

no code implementations COLING 2020 Kexiang Wang, Tianyu Liu, Baobao Chang, Zhifang Sui

The widespread adoption of reference-based automatic evaluation metrics such as ROUGE has promoted the development of document summarization.

Document Summarization

Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference

1 code implementation EMNLP 2020 Xiaoan Ding, Tianyu Liu, Baobao Chang, Zhifang Sui, Kevin Gimpel

We explore training objectives for discriminative fine-tuning of our generative classifiers, showing improvements over log loss fine-tuning from prior work .

Natural Language Inference

An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference

1 code implementation CONLL 2020 Tianyu Liu, Xin Zheng, Xiaoan Ding, Baobao Chang, Zhifang Sui

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust.

Data Augmentation Natural Language Inference

HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference

no code implementations LREC 2020 Tianyu Liu, Xin Zheng, Baobao Chang, Zhifang Sui

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise.

Natural Language Inference

Pun-GAN: Generative Adversarial Network for Pun Generation

1 code implementation IJCNLP 2019 Fuli Luo, Shunyao Li, Pengcheng Yang, Lei LI, Baobao Chang, Zhifang Sui, Xu sun

It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.

Learning to Control the Fine-grained Sentiment for Story Ending Generation

no code implementations ACL 2019 Fuli Luo, Damai Dai, Pengcheng Yang, Tianyu Liu, Baobao Chang, Zhifang Sui, Xu sun

Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.

Text Generation

Towards Comprehensive Description Generation from Factual Attribute-value Tables

no code implementations ACL 2019 Tianyu Liu, Fuli Luo, Pengcheng Yang, Wei Wu, Baobao Chang, Zhifang Sui

To relieve these problems, we first propose force attention (FA) method to encourage the generator to pay more attention to the uncovered attributes to avoid potential key attributes missing.

A Soft Label Strategy for Target-Level Sentiment Classification

no code implementations WS 2019 Da Yin, Xiao Liu, Xiuyu Wu, Baobao Chang

In this paper, we propose a soft label approach to target-level sentiment classification task, in which a history-based soft labeling model is proposed to measure the possibility of a context word as an opinion word.

Classification General Classification +1

A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

2 code implementations24 May 2019 Fuli Luo, Peng Li, Jie zhou, Pengcheng Yang, Baobao Chang, Zhifang Sui, Xu sun

Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.

Text Style Transfer Unsupervised Text Style Transfer

Incorporating Glosses into Neural Word Sense Disambiguation

1 code implementation ACL 2018 Fuli Luo, Tianyu Liu, Qiaolin Xia, Baobao Chang, Zhifang Sui

GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.

Word Sense Disambiguation

Table-to-text Generation by Structure-aware Seq2seq Learning

3 code implementations27 Nov 2017 Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, Zhifang Sui

In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table.

Table-to-Text Generation

A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction

no code implementations EMNLP 2017 Tianyu Liu, Kexiang Wang, Baobao Chang, Zhifang Sui

Distant-supervised relation extraction inevitably suffers from wrong labeling problems because it heuristically labels relational facts with knowledge bases.

Relation Extraction

Order-Planning Neural Text Generation From Structured Data

1 code implementation1 Sep 2017 Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui

Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.

Question Answering Table-to-Text Generation

Syntax Aware LSTM model for Semantic Role Labeling

no code implementations WS 2017 Feng Qian, Lei Sha, Baobao Chang, Lu-chen Liu, Ming Zhang

In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models.

Feature Engineering Machine Translation +2

Syntax Aware LSTM Model for Chinese Semantic Role Labeling

no code implementations3 Apr 2017 Feng Qian, Lei Sha, Baobao Chang, Lu-chen Liu, Ming Zhang

As for semantic role labeling (SRL) task, when it comes to utilizing parsing information, both traditional methods and recent recurrent neural network (RNN) based methods use the feature engineering way.

Chinese Semantic Role Labeling Dependency Parsing +2

Improving Chinese SRL with Heterogeneous Annotations

no code implementations22 Feb 2017 Qiaolin Xia, Baobao Chang, Zhifang Sui

Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus.

Chinese Semantic Role Labeling Semantic Role Labeling

Event Detection with Burst Information Networks

no code implementations COLING 2016 Tao Ge, Lei Cui, Baobao Chang, Zhifang Sui, Ming Zhou

Retrospective event detection is an important task for discovering previously unidentified events in a text stream.

Event Detection

Reading and Thinking: Re-read LSTM Unit for Textual Entailment Recognition

no code implementations COLING 2016 Lei Sha, Baobao Chang, Zhifang Sui, Sujian Li

After read the premise again, the model can get a better understanding of the premise, which can also affect the understanding of the hypothesis.

Information Retrieval Machine Translation +3

Towards Time-Aware Knowledge Graph Completion

no code implementations COLING 2016 Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, Zhifang Sui

In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts.

Knowledge Graph Completion Question Answering +1

Aligning Coordinated Text Streams through Burst Information Network Construction and Decipherment

no code implementations27 Sep 2016 Tao Ge, Qing Dou, Xiaoman Pan, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Ming Zhou

We introduce a novel Burst Information Network (BINet) representation that can display the most important information and illustrate the connections among bursty entities, events and keywords in the corpus.

Decipherment

Joint Learning Templates and Slots for Event Schema Induction

no code implementations NAACL 2016 Lei Sha, Sujian Li, Baobao Chang, Zhifang Sui

Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type.

Semantic Segmentation

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