Minlie Huang


2019

pdf bib
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog
Ryuichi Takanobu | Hanlin Zhu | Minlie Huang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Dialog policy decides what and how a task-oriented dialog system will respond, and plays a vital role in delivering effective conversations. Many studies apply Reinforcement Learning to learn a dialog policy with the reward function which requires elaborate design and pre-specified user goals. With the growing needs to handle complex goals across multiple domains, such manually designed reward functions are not affordable to deal with the complexity of real-world tasks. To this end, we propose Guided Dialog Policy Learning, a novel algorithm based on Adversarial Inverse Reinforcement Learning for joint reward estimation and policy optimization in multi-domain task-oriented dialog. The proposed approach estimates the reward signal and infers the user goal in the dialog sessions. The reward estimator evaluates the state-action pairs so that it can guide the dialog policy at each dialog turn. Extensive experiments on a multi-domain dialog dataset show that the dialog policy guided by the learned reward function achieves remarkably higher task success than state-of-the-art baselines.

pdf bib
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
Zhihong Shao | Minlie Huang | Jiangtao Wen | Wenfei Xu | xiaoyan zhu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.

pdf bib
ARAML: A Stable Adversarial Training Framework for Text Generation
Pei Ke | Fei Huang | Minlie Huang | xiaoyan zhu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Most of the existing generative adversarial networks (GAN) for text generation suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. To tackle this problem, we propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML). During adversarial training, the discriminator assigns rewards to samples which are acquired from a stationary distribution near the data rather than the generator’s distribution. The generator is optimized with maximum likelihood estimation augmented by the discriminator’s rewards instead of policy gradient. Experiments show that our model can outperform state-of-the-art text GANs with a more stable training process.

pdf bib
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Chujie Zheng | Minlie Huang | Aixin Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora. In this paper we propose a large-scale Chinese cloze test dataset ChID, which studies the comprehension of idiom, a unique language phenomenon in Chinese. In this corpus, the idioms in a passage are replaced by blank symbols and the correct answer needs to be chosen from well-designed candidate idioms. We carefully study how the design of candidate idioms and the representation of idioms affect the performance of state-of-the-art models. Results show that the machine accuracy is substantially worse than that of human, indicating a large space for further research.

pdf bib
ConvLab: Multi-Domain End-to-End Dialog System Platform
Sungjin Lee | Qi Zhu | Ryuichi Takanobu | Zheng Zhang | Yaoqin Zhang | Xiang Li | Jinchao Li | Baolin Peng | Xiujun Li | Minlie Huang | Jianfeng Gao
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.

2018

pdf bib
An Operation Network for Abstractive Sentence Compression
Naitong Yu | Jie Zhang | Minlie Huang | Xiaoyan Zhu
Proceedings of the 27th International Conference on Computational Linguistics

Sentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase the words, which are more coherent to human sentence compression. In this paper, we propose Operation Network, a neural network approach for abstractive sentence compression, which combines the advantages of both delete-based and generate-based sentence compression models. The central idea of Operation Network is to model the sentence compression process as an editing procedure. First, unnecessary words are deleted from the source sentence, then new words are either generated from a large vocabulary or copied directly from the source sentence. A compressed sentence can be obtained by a series of such edit operations (delete, copy and generate). Experiments show that Operation Network outperforms state-of-the-art baselines.

pdf bib
An Interpretable Reasoning Network for Multi-Relation Question Answering
Mantong Zhou | Minlie Huang | Xiaoyan Zhu
Proceedings of the 27th International Conference on Computational Linguistics

Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis, thereby allowing manual manipulation in predicting the final answer.

pdf bib
Generating Informative Responses with Controlled Sentence Function
Pei Ke | Jian Guan | Minlie Huang | Xiaoyan Zhu
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentence function is a significant factor to achieve the purpose of the speaker, which, however, has not been touched in large-scale conversation generation so far. In this paper, we present a model to generate informative responses with controlled sentence function. Our model utilizes a continuous latent variable to capture various word patterns that realize the expected sentence function, and introduces a type controller to deal with the compatibility of controlling sentence function and generating informative content. Conditioned on the latent variable, the type controller determines the type (i.e., function-related, topic, and ordinary word) of a word to be generated at each decoding position. Experiments show that our model outperforms state-of-the-art baselines, and it has the ability to generate responses with both controlled sentence function and informative content.

pdf bib
Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
Yansen Wang | Chenyi Liu | Minlie Huang | Liqiang Nie
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.

2017

pdf bib
Linguistically Regularized LSTM for Sentiment Classification
Qiao Qian | Minlie Huang | Jinhao Lei | Xiaoyan Zhu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which has remarkably degraded performance when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words). In this paper, we propose simple models trained with sentence-level annotation, but also attempt to model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are able to capture the linguistic role of sentiment words, negation words, and intensity words in sentiment expression.

2016

pdf bib
Attention-based LSTM for Aspect-level Sentiment Classification
Yequan Wang | Minlie Huang | Xiaoyan Zhu | Li Zhao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
GAKE: Graph Aware Knowledge Embedding
Jun Feng | Minlie Huang | Yang Yang | Xiaoyan Zhu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Knowledge embedding, which projects triples in a given knowledge base to d-dimensional vectors, has attracted considerable research efforts recently. Most existing approaches treat the given knowledge base as a set of triplets, each of whose representation is then learned separately. However, as a fact, triples are connected and depend on each other. In this paper, we propose a graph aware knowledge embedding method (GAKE), which formulates knowledge base as a directed graph, and learns representations for any vertices or edges by leveraging the graph’s structural information. We introduce three types of graph context for embedding: neighbor context, path context, and edge context, each reflects properties of knowledge from different perspectives. We also design an attention mechanism to learn representative power of different vertices or edges. To validate our method, we conduct several experiments on two tasks. Experimental results suggest that our method outperforms several state-of-art knowledge embedding models.

pdf bib
Product Review Summarization by Exploiting Phrase Properties
Naitong Yu | Minlie Huang | Yuanyuan Shi | Xiaoyan Zhu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We propose a phrase-based approach for generating product review summaries. The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary. Specifically, we exploit two phrase properties, popularity and specificity. Popularity describes how popular the phrase is in the original reviews. Specificity describes how descriptive a phrase is in comparison to generic comments. We formalize the phrase selection procedure as an optimization problem and solve it using integer linear programming (ILP). An aspect-based bigram language model is used for generating the final summary with the selected phrases. Experiments show that our summarizer outperforms the other baselines.

pdf bib
Context-aware Natural Language Generation for Spoken Dialogue Systems
Hao Zhou | Minlie Huang | Xiaoyan Zhu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Natural language generation (NLG) is an important component of question answering(QA) systems which has a significant impact on system quality. Most tranditional QA systems based on templates or rules tend to generate rigid and stylised responses without the natural variation of human language. Furthermore, such methods need an amount of work to generate the templates or rules. To address this problem, we propose a Context-Aware LSTM model for NLG. The model is completely driven by data without manual designed templates or rules. In addition, the context information, including the question to be answered, semantic values to be addressed in the response, and the dialogue act type during interaction, are well approached in the neural network model, which enables the model to produce variant and informative responses. The quantitative evaluation and human evaluation show that CA-LSTM obtains state-of-the-art performance.

pdf bib
A Sentence Interaction Network for Modeling Dependence between Sentences
Biao Liu | Minlie Huang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
TransG : A Generative Model for Knowledge Graph Embedding
Han Xiao | Minlie Huang | Xiaoyan Zhu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

pdf bib
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network
Qiao Qian | Bo Tian | Minlie Huang | Yang Liu | Xuan Zhu | Xiaoyan Zhu
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

pdf bib
Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency
Li Zhao | Minlie Huang | Haiqiang Chen | Junjun Cheng | Xiaoyan Zhu
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

pdf bib
New Word Detection for Sentiment Analysis
Minlie Huang | Borui Ye | Yichen Wang | Haiqiang Chen | Junjun Cheng | Xiaoyan Zhu
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

pdf bib
Fine Granular Aspect Analysis using Latent Structural Models
Lei Fang | Minlie Huang
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

pdf bib
Quality-biased Ranking of Short Texts in Microblogging Services
Minlie Huang | Yi Yang | Xiaoyan Zhu
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

pdf bib
Structure-Aware Review Mining and Summarization
Fangtao Li | Chao Han | Minlie Huang | Xiaoyan Zhu | Ying-Ju Xia | Shu Zhang | Hao Yu
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

pdf bib
Learning to Annotate Scientific Publications
Minlie Huang | Zhiyong Lu
Coling 2010: Posters

pdf bib
A Comparative Study on Ranking and Selection Strategies for Multi-Document Summarization
Feng Jin | Minlie Huang | Xiaoyan Zhu
Coling 2010: Posters

pdf bib
Recognizing Biomedical Named Entities Using Skip-Chain Conditional Random Fields
Jingchen Liu | Minlie Huang | Xiaoyan Zhu
Proceedings of the 2010 Workshop on Biomedical Natural Language Processing

pdf bib
Learning to Link Entities with Knowledge Base
Zhicheng Zheng | Fangtao Li | Minlie Huang | Xiaoyan Zhu
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

pdf bib
Metadata-Aware Measures for Answer Summarization in Community Question Answering
Mattia Tomasoni | Minlie Huang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

pdf bib
Answering Opinion Questions with Random Walks on Graphs
Fangtao Li | Yang Tang | Minlie Huang | Xiaoyan Zhu
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

pdf bib
Towards Automatic Generation of Gene Summary
Feng Jin | Minlie Huang | Zhiyong Lu | Xiaoyan Zhu
Proceedings of the BioNLP 2009 Workshop

2004

pdf bib
Discovering Patterns to Extract Protein-Protein Interactions from Full Biomedical Texts
Minlie Huang | Xiaoyan Zhu | Donald G. Payan | Kunbin Qu | Ming Li
Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA/BioNLP)