A Corpus-free State2Seq User Simulator for Task-oriented Dialogue

Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotated dialogue corpora are often unavailable. One of the popular approaches addressing this is to train a dialogue agent with a user simulator. Traditional user simulators are built upon a set of dialogue rules and therefore lack response diversity. This severely limits the simulated cases for agent training. Later data-driven user models work better in diversity but suffer from data scarcity problem. To remedy this, we design a new corpus-free framework that taking advantage of their benefits. The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data. To enhance the performance, we propose the State2Seq user simulator model to efficiently leverage dialogue state and history. Experiment results on an open dataset show that our user simulator helps agents achieve an improvement of 6.36% on success rate. State2Seq model outperforms the seq2seq baseline for 1.9 F-score.Comment: Accepted by CCL201

Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - the MultiWOZ dataset and the Persona chat dataset

Chinese Computational Linguistics [electronic resource] : 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings /

This book constitutes the proceedings of the 18th China National Conference on Computational Linguistics, CCL 2019, held in Kunming, China, in October 2019. The 56 full papers presented in this volume were carefully reviewed and selected from 134 submissions. They were organized in topical sections named: linguistics and cognitive science, fundamental theory and methods of computational linguistics, information retrieval and question answering, text classification and summarization, knowledge graph and information extraction, machine translation and multilingual information processing, minority language processing, language resource and evaluation, social computing and sentiment analysis, NLP applications.Colligational Patterns in China English: the Case of the Verbs of Communication -- Testing the Reasoning Power for NLI Models with Annotated Multi-perspective Entailment Dataset -- Enhancing Chinese Word Embeddings from Relevant Derivative Meanings of Main-Components in Characters -- Association Relationship Analyses of Stylistic Syntactic Structures -- BB-KBQA: BERT-Based Knowledge Base Question Answering -- ERCNN: Enhanced Recurrent Convolutional Neural Networks for Learning Sentence Similarity -- Improving a Syntactic Graph Convolution Network for Sentence Compression -- Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationship Parser -- Syntax-Aware Attention for Natural Language Inference with Phrase-Level Matching -- Sharing Pre-trained BERT Decoder for a Hybrid Summarization -- Title-Aware Neural News Topic Prediction -- How to Fine-Tune BERT for Text Classification? -- A Comprehensive Verification of Transformer in Text Classification -- Improving Relation Extraction with Relation-Based Gated Convolutional Selector -- Attention-based Gated Convolutional Neural Networks for Distant Supervised Relation Extraction -- Relation and Fact Type Supervised Knowledge Graph Embedding via Weighted Scores -- Leveraging Multi-Head Attention Mechanism to Improve Event Detection -- Short-Text Conceptualization Based on A Co-Ranking Framework via Lexical Knowledge Base -- Denoising Distant Supervision for Relation Extraction with Entropy Weight Method -- Cross-view Adaptation Network for Cross-domain Relation Extraction -- Mongolian-Chinese Unsupervised Neural Machine Translation with Lexical Feature -- Learning Multilingual Sentence Embeddings from Monolingual Corpus -- Chinese Historical Term Translation Pairs Extraction Using Modern Chinese As a Pivot Language -- Construction of an English-Uyghur WordNet Dataset -- Endangered Tujia Language Speech Enhancement Research Based on Improved DCGAN -- Research for Tibetan-Chinese Name Transliteration Based on Multi-granularity -- An End-to-end Method for Data Filtering on Tibetan-Chinese Parallel Corpus via Negative Sampling -- An Attention-Based Approach for Mongolian News Named Entity Recognition -- On the Semi-unsupervised Construction of Auto-Keyphrases Corpus from Large-scale Chinese Automobile E-Commerce Reviews -- Multi-label Aspect Classification on Question-Answering Text with Contextualized Attention-based Neural Network -- A Document Driven Dialogue Generation Model -- Capsule Networks for Chinese Opinion Questions Machine Reading Comprehension -- Table-to-Text Generation via Row-Aware Hierarchical Encoder -- Dropped Pronoun Recovery in Chinese Conversations with Knowledge-enriched Neural Network -- Automatic Judgment Prediction via Legal Reading Comprehension -- Legal Cause Prediction with Inner Descriptions and Outer Hierarchies -- Natural Language Inference based on the LIC Architecture with DCAE Feature -- Neural CTR Prediction for Native Ad -- Depression Detection on Social Media with Reinforcement Learning -- How Important is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks -- Graph Neural Net-based User Simulator -- Pinyin as A Feature of Neural Machine Translation for Chinese Speech Recognition Error Correction -- Neural Gender Prediction from News Browsing Data -- A Top-down Model for Character-level Chinese Dependency Parsing -- A Corpus-free State2Seq User Simulator for Task-oriented Dialogue -- Utterance Alignment in Custom Service by Integer Programming.This book constitutes the proceedings of the 18th China National Conference on Computational Linguistics, CCL 2019, held in Kunming, China, in October 2019. The 56 full papers presented in this volume were carefully reviewed and selected from 134 submissions. They were organized in topical sections named: linguistics and cognitive science, fundamental theory and methods of computational linguistics, information retrieval and question answering, text classification and summarization, knowledge graph and information extraction, machine translation and multilingual information processing, minority language processing, language resource and evaluation, social computing and sentiment analysis, NLP applications