Ming Liu


2020

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Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure
Jiaqi Li | Ming Liu | Min-Yen Kan | Zihao Zheng | Zekun Wang | Wenqiang Lei | Ting Liu | Bing Qin
Proceedings of the 28th International Conference on Computational Linguistics

Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni’s source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F1 on Molweni’s questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.

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Monash-Summ@LongSumm 20 SciSummPip: An Unsupervised Scientific Paper Summarization Pipeline
Jiaxin Ju | Ming Liu | Longxiang Gao | Shirui Pan
Proceedings of the First Workshop on Scholarly Document Processing

The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task. It contains three shared tasks and we participate in the LongSumm shared task. In this paper, we describe our text summarization system, SciSummPip, inspired by SummPip (Zhao et al., 2020) that is an unsupervised text summarization system for multi-document in News domain. Our SciSummPip includes a transformer-based language model SciBERT (Beltagy et al., 2019) for contextual sentence representation, content selection with PageRank (Page et al., 1999), sentence graph construction with both deep and linguistic information, sentence graph clustering and within-graph summary generation. Our work differs from previous method in that content selection and a summary length constraint is applied to adapt to the scientific domain. The experiment results on both training dataset and blind test dataset show the effectiveness of our method, and we empirically verify the robustness of modules used in SciSummPip with BERTScore (Zhang et al., 2019a).

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Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
Jueqing Lu | Lan Du | Ming Liu | Joanna Dipnall
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III ) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.

2019

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Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification
Chunning Du | Haifeng Sun | Jingyu Wang | Qi Qi | Jianxin Liao | Tong Xu | Ming Liu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context. The main challenge comes from multi-aspect sentences, which express multiple sentiment polarities towards different targets, resulting in overlapped feature representation. However, most existing neural models tend to utilize static pooling operation or attention mechanism to identify sentimental words, which therefore insufficient for dealing with overlapped features. To solve this problem, we propose to utilize capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm. Furthermore, interactive attention mechanism is introduced in the capsule routing procedure to model the semantic relationship between aspect terms and context. The iterative routing also enables encoding sentence from a global perspective. Experimental results on three datasets show that our proposed model achieves state-of-the-art performance.

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Learning How to Active Learn by Dreaming
Thuy-Trang Vu | Ming Liu | Dinh Phung | Gholamreza Haffari
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. Recent data-driven AL policy learning methods are also restricted to learn from closely related domains. We introduce a new sample-efficient method that learns the AL policy directly on the target domain of interest by using wake and dream cycles. Our approach interleaves between querying the annotation of the selected datapoints to update the underlying student learner and improving AL policy using simulation where the current student learner acts as an imperfect annotator. We evaluate our method on cross-domain and cross-lingual text classification and named entity recognition tasks. Experimental results show that our dream-based AL policy training strategy is more effective than applying the pretrained policy without further fine-tuning and better than the existing strong baseline methods that use heuristics or reinforcement learning.

2018

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Learning to Actively Learn Neural Machine Translation
Ming Liu | Wray Buntine | Gholamreza Haffari
Proceedings of the 22nd Conference on Computational Natural Language Learning

Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a framework to learn sentence selection strategies for neural MT. We train the AL query strategy using a high-resource language-pair based on AL simulations, and then transfer it to the low-resource language-pair of interest. The learned query strategy capitalizes on the shared characteristics between the language pairs to make an effective use of the AL budget. Our experiments on three language-pairs confirms that our method is more effective than strong heuristic-based methods in various conditions, including cold-start and warm-start as well as small and extremely small data conditions.

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Learning How to Actively Learn: A Deep Imitation Learning Approach
Ming Liu | Wray Buntine | Gholamreza Haffari
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL “policy” using “imitation learning” (IL). Our IL-based approach makes use of an efficient and effective “algorithmic expert”, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.

2017

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Leveraging Linguistic Resources for Improving Neural Text Classification
Ming Liu | Gholamreza Haffari | Wray Buntine | Michelle Ananda-Rajah
Proceedings of the Australasian Language Technology Association Workshop 2017

2016

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Learning cascaded latent variable models for biomedical text classification
Ming Liu | Gholamreza Haffari | Wray Buntine
Proceedings of the Australasian Language Technology Association Workshop 2016

2013

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Multimodal DBN for Predicting High-Quality Answers in cQA portals
Haifeng Hu | Bingquan Liu | Baoxun Wang | Ming Liu | Xiaolong Wang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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PAL: A Chatterbot System for Answering Domain-specific Questions
Yuanchao Liu | Ming Liu | Xiaolong Wang | Limin Wang | Jingjing Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Whitepaper of NEWS 2012 Shared Task on Machine Transliteration
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

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Report of NEWS 2012 Machine Transliteration Shared Task
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

2011

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Report of NEWS 2011 Machine Transliteration Shared Task
Min Zhang | Haizhou Li | A Kumaran | Ming Liu
Proceedings of the 3rd Named Entities Workshop (NEWS 2011)

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Joint Alignment and Artificial Data Generation: An Empirical Study of Pivot-based Machine Transliteration
Min Zhang | Xiangyu Duan | Ming Liu | Yunqing Xia | Haizhou Li
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Improving Name Origin Recognition with Context Features and Unlabelled Data
Vladimir Pervouchine | Min Zhang | Ming Liu | Haizhou Li
Coling 2010: Posters