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8th ccf international conference on natural language processing and chinese computing, nlpcc 2019

8th CCF International Conference on Natural Language Process...

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8th ccf international conference on natural language processing and chinese computing, nlpcc 2019

ISBN: (纸本)9783030322359

the proceedings contain 65 papers. the special focus in this conference is on natural language processing and chinese computing. the topics include: Co-attention and Aggregation Based chinese Recognizing Textual Entailment Model;a Knowledge-Gated Mechanism for Utterance Domain Classification;automatic Translating Between Ancient chinese and Contemporary chinese with Limited Aligned Corpora;FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition;domain Adaptive Question Answering over Knowledge Base;extending the Transformer with Context and Multi-dimensional Mechanism for Dialogue Response Generation;co-attention Networks for Aspect-Level Sentiment Analysis;hierarchical-Gate Multimodal Network for Human Communication Comprehension;end-to-End Model for Offline Handwritten Mongolian Word Recognition;dynamic Label Correction for Distant Supervision Relation Extraction via Semantic Similarity;A Recursive Information Flow Gated Model for RST-Style Text-Level Discourse Parsing;relation Classification in Scientific Papers Based on Convolutional Neural Network;effective Soft-Adaptation for Neural Machine Translation;neural Machine Translation with Bilingual History Involved Attention;joint Modeling of Recognizing Macro chinese Discourse Nuclearity and Relation Based on Structure and Topic Gated Semantic Network;attentional Neural Network for Emotion Detection in Conversations with Speaker Influence Awareness;triple-Joint Modeling for Question Generation Using Cross-Task Autoencoder;deep Multi-task Learning with Cross Connected Layer for Slot Filling;a Sequence-to-Action Architecture for Character-Based chinese Dependency Parsing with Status History;a New Fine-Tuning Architecture Based on Bert for Word Relation Extraction;classification over Clustering: Augmenting Text Representation with Clusters Helps!;rumor Detection with Hierarchical Recurrent Convolutional Neural Network;automatic Proofreading in chinese: Detect and Correct Spelling Errors in Character

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8th ccf international conference on natural language processing and chinese computing, nlpcc 2019

8th CCF International Conference on Natural Language Process...

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8th ccf international conference on natural language processing and chinese computing, nlpcc 2019

ISBN: (纸本)9783030322328

the proceedings contain 65 papers. the special focus in this conference is on natural language processing and chinese computing. the topics include: Using Bidirectional Transformer-CRF for Spoken language Understanding;evaluating and Enhancing the Robustness of Retrieval-Based Dialogue Systems with Adversarial Examples;Many vs. Many Query Matching with Hierarchical BERT and Transformer;a Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph;feature-Level Attention Based Sentence Encoding for Neural Relation Extraction;domain Representation for Knowledge Graph Embedding;evidence Distilling for Fact Extraction and Verification;KG-to-Text Generation with Slot-Attention and Link-Attention;group-Constrained Embedding of Multi-fold Relations in Knowledge Bases;improving Question Answering by Commonsense-Based Pre-training;cosine-Based Embedding for Completing Schematic Knowledge;improved DeepWalk Algorithm Based on Preference Random Walk;learning Stance Classification with Recurrent Neural Capsule Network;learning Unsupervised Word Mapping via Maximum Mean Discrepancy;solving chinese Character Puzzles Based on Character Strokes;improving Multi-head Attention with Capsule Networks;REET: Joint Relation Extraction and Entity Typing via Multi-task Learning;detecting and Translating Dropped Pronouns in Neural Machine Translation;select the Best Translation from Different Systems Without Reference;word Position Aware Translation Memory for Neural Machine Translation;Multi-strategies Method for Cold-Start Stage Question Matching of rQA Task;cross Aggregation of Multi-head Attention for Neural Machine Translation;target Oriented Data Generation for Quality Estimation of Machine Translation;improved Quality Estimation of Machine Translation with Pre-trained language Representation;an Analytical Study on a Benchmark Corpus Constructed for Related Work Generation;automated thematic and Emotional Modern chinese Poetry Composition;charge Prediction with Legal

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Retrieval-Augmented Code Generation for Universal Information Extraction 13th

Retrieval-Augmented Code Generation for Universal Informatio...

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13th international conference on natural language processing and chinese computing

作者： Guo, Yucan Li, Zixuan Jin, Xiaolong Liu, Yantao Zeng, Yutao Liu, Wenxuan Li, Xiang Yang, Pan Bai, Long Guo, Jiafeng Chen, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts. Recently, Large language Models (LLMs) with code-style prompts have demonstrated powerful capabilities in IE tasks. However, adopting code LLMs to conduct IE tasks still has two challenges: (1) It still lacks a unified code-style prompt for different IE tasks since existing methods use task-specific prompts for separate IE tasks. (2) It still lacks an effective in-context learning (ICL) method to encourage LLMs to conduct IE tasks precisely, considering some powerful LLMs are close-sourced and not trainable. therefore, this paper proposes a code generation framework for Universal IE (UIE) tasks called Code4UIE. Specifically, for the first challenge, Code4UIE designs a unified code-style schema for various IE tasks via Python classes. By so doing, different IE tasks can be associated, and LLMs can learn from various IE tasks effectively. For the second challenge, Code4UIE adopts a retrieval-augmented mechanism to comprehensively utilize the ICL ability of LLMs. Extensive experiments on five representative IE tasks across nine datasets demonstrate the effectiveness of the Code4UIE framework.

关键词： Information Extraction Code Generation Large language Model Information Retrieval

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A Study on Prosodic Distribution of Yes/No Questions with Focus in Mandarin 8th

A Study on Prosodic Distribution of Yes/No Questions with Fo...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Huang, Jingwen Zhang, Gaoyuan Beijing Language & Culture Univ Beijing Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9783030322335;9783030322328

chinese intonation is reflected by adjusting the types of pitch, duration and intensity variants of a series of syllables. the purpose of present study is to compare the difference between yes/no questions with or without particle. the method of experimental phonetics was applied to investigate the performance of pitch, duration and intensity of questions. the results showed that since the particle ("(sic)") played the main role in conveying the question information, it is not necessary for the particle to enhance its prosodic elements at the same time, while the question without particle had to improve its pitch, duration and intensity of the final meaningful syllable to realize the interrogative effect.

关键词： Yes/No Question Particle Focus Prosodic elements

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Variational Attention for Commonsense Knowledge Aware Conversation Generation 8th

Variational Attention for Commonsense Knowledge Aware Conver...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Bai, Guirong He, Shizhu Liu, Kang Zhao, Jun Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Conversation generation is an important task in natural language processing, and commonsense knowledge is vital to provide a shared background for better replying. In this paper, we present a novel commonsense knowledge aware conversation generation model, which adopts variational attention for incorporating commonsense knowledge to generate more appropriate conversation. Given a post, the model retrieves relevant knowledge graphs from a knowledge base, and then attentively incorporates knowledge to its response. For enhancing attention to incorporate more clean and suitable knowledge into response generation, we adopt variational attention rather than standard neural attention on knowledge graphs, which is unlike previous knowledge aware generation models. Experimental results show that the variational attention based model can incorporate more clean and suitable knowledge into response generation.

关键词： Conversation generation Commonsense knowledge Variational attention

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Improving Multi-head Attention with Capsule Networks 8th

Improving Multi-head Attention with Capsule Networks

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Gu, Shuhao Feng, Yang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Multi-head attention advances neural machine translation by working out multiple versions of attention in different subspaces, but the neglect of semantic overlapping between subspaces increases the difficulty of translation and consequently hinders the further improvement of translation performance. In this paper, we employ capsule networks to comb the information from the multiple heads of the attention so that similar information can be clustered and unique information can be reserved. To this end, we adopt two routing mechanisms of Dynamic Routing and EM Routing, to fulfill the clustering and separating. We conducted experiments on chinese-to-English and English-to-German translation tasks and got consistent improvements over the strong Transformer baseline.

关键词： Neural machine translation Transformer Capsule network Multi-head attention

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Constructing chinese Macro Discourse Tree via Multiple Views and Word Pair Similarity 8th

Constructing Chinese Macro Discourse Tree via Multiple Views...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Zhou, Yi Chu, Xiaomin Li, Peifeng Zhu, Qiaoming Soochow Univ Sch Comp Sci & Technol Suzhou Jiangsu Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Macro-discourse structure recognition is an important task in macro-discourse analysis. At present, the research on macro-discourse analysis mostly uses the manual features (e.g., the position features), and ignores the semantic information in topic level. In this paper, we first propose a multi-view neural network to construct chinese macro discourse trees from three views, i.e., the word view, the context view and the topic view. Besides, we propose a novel word-pair similarity mechanism to capture the interaction among the discourse units and the topic. the experimental results on MCDTB, a chinese discourse corpus, show that our model outperforms the baseline significantly.

关键词： Macro discourse Discourse tree construction Word-pair similarity Multiple views

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Automated thematic and Emotional Modern chinese Poetry Composition 8th

Automated Thematic and Emotional Modern Chinese Poetry Compo...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Guo, Xiaoyu Chen, Meng Song, Yang He, Xiaodong Zhou, Bowen JD AI Beijing Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Topic and emotion are two essential elements in poetry creation, and also have critical impact on the quality of poetry. Inspired by this motivation, we propose a novel model to inject rich topics and emotions into modern chinese poetry generation simultaneously in this paper. For this purpose, our model leverages three novel mechanisms including (1) learning specific emotion embeddings and incorporate them into decoding process;(2) mining latent topics and encode them via a joint attention mechanism;and (3) enhancing content diversity by encouraging coverage scores in beam search process. Experimental results show that our proposed model can not only generate poems with rich topics and emotions, but can also improve the poeticness of generated poems significantly.

关键词： Poetry generation Deep learning Sequence to sequence model

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Automatically Build Corpora for chinese Spelling Check Based on the Input Method 8th

Automatically Build Corpora for Chinese Spelling Check Based...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Duan, Jianyong Pan, Lijian Wang, Hao Zhang, Mei Wu, Mingli North China Univ Technol Beijing Peoples R China CNONIX Natl Stand Applicat & Promot Lab Beijing Peoples R China

ISBN: (纸本)9783030322335;9783030322328

chinese Spelling Check (CSC) is very important for chinese language processing. To utilize supervised learning for CSC, one of the main challenges is that high-quality annotated corpora are not enough in building models. this paper proposes new approaches to automatically build the corpora of CSC based on the input method. We build two corpora: one is used to check the errors in the texts generated by the Pinyin input method, called p-corpus, and the other is used to check the errors in the texts generated by the voice input method, called v-corpus. the p-corpus is constructed using two methods, one is based on the conversion between chinese characters and the sounds of the characters, and the other is based on Automatic Speech Recognition (ASR). the v-corpus is constructed based on ASR. We use the misspelled sentences in real language situation as the test set. Experimental results demonstrate that our corpora can get a better checking effect than the benchmark corpus.

关键词： Corpora chinese spelling check Input method

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Using Bidirectional Transformer-CRF for Spoken language Understanding 8th

Using Bidirectional Transformer-CRF for Spoken Language Unde...

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8th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Zhang, Linhao Wang, Houfeng Peking Univ MOE Key Lab Computat Linguist Beijing 100871 Peoples R China

ISBN: (纸本)9783030322335;9783030322328

Spoken language Understanding (SLU) is a critical component in spoken dialogue systems. It is typically composed of two tasks: intent detection (ID) and slot filling (SF). Currently, most effective models carry out these two tasks jointly and often result in better performance than separate models. However, these models usually fail to model the interaction between intent and slots and ties these two tasks only by a joint loss function. In this paper, we propose a new model based on bidirectional Transformer and introduce a padding method, enabling intent and slots to interact with each other in an effective way. A CRF layer is further added to achieve global optimization. We conduct our experiments on benchmark ATIS and Snips datasets, and results show that our model achieves state-of-the-art on both tasks.

关键词： SLU Transformer CRF Joint method

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