Wei-Yun Ma

Also published as: Wei Yun Ma


2021

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H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction
Jhih-wei Chen | Tsu-Jui Fu | Chen-Kang Lee | Wei-Yun Ma
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline
Mu Yang | Chi-Yen Chen | Yi-Hui Lee | Qian-hui Zeng | Wei-Yun Ma | Chen-Yang Shih | Wei-Jhih Chen
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper, we design headword-oriented entity linking (HEL), a specialized entity linking problem in which only the headwords of the entities are to be linked to knowledge bases; mention scopes of the entities do not need to be identified in the problem setting. This special task is motivated by the fact that in many articles referring to specific products, the complete full product names are rarely written; instead, they are often abbreviated to shorter, irregular versions or even just to their headwords, which are usually their product types, such as “stick” or “mask” in a cosmetic context. To fully design the special task, we construct a labeled cosmetic corpus as a public benchmark for this problem, and propose a product embedding model to address the task, where each product corresponds to a dense representation to encode the different information on products and their context jointly. Besides, to increase training data, we propose a special transfer learning framework in which distant supervision with heuristic patterns is first utilized, followed by supervised learning using a small amount of manually labeled data. The experimental results show that our model provides a strong benchmark performance on the special task.

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CA-EHN: Commonsense Analogy from E-HowNet
Peng-Hsuan Li | Tsan-Yu Yang | Wei-Yun Ma
Proceedings of the 12th Language Resources and Evaluation Conference

Embedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words with only dozens of pre-defined relations, mostly morphological relations and named entities. In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. We present CA-EHN, the first commonsense word analogy dataset containing 90,505 analogies covering 5,656 words and 763 relations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense knowledge. The dataset is publicly available at https://github.com/ckiplab/CA-EHN.

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Semantic Guidance of Dialogue Generation with Reinforcement Learning
Cheng-Hsun Hsueh | Wei-Yun Ma
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Neural encoder-decoder models have shown promising performance for human-computer dialogue systems over the past few years. However, due to the maximum-likelihood objective for the decoder, the generated responses are often universal and safe to the point that they lack meaningful information and are no longer relevant to the post. To address this, in this paper, we propose semantic guidance using reinforcement learning to ensure that the generated responses indeed include the given or predicted semantics and that these semantics do not appear repeatedly in the response. Synsets, which comprise sets of manually defined synonyms, are used as the form of assigned semantics. For a given/assigned/predicted synset, only one of its synonyms should appear in the generated response; this constitutes a simple but effective semantic-control mechanism. We conduct both quantitative and qualitative evaluations, which show that the generated responses are not only higher-quality but also reflect the assigned semantic controls.

2019

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iComposer: An Automatic Songwriting System for Chinese Popular Music
Hsin-Pei Lee | Jhih-Sheng Fang | Wei-Yun Ma
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

In this paper, we introduce iComposer, an interactive web-based songwriting system designed to assist human creators by greatly simplifying music production. iComposer automatically creates melodies to accompany any given text. It also enables users to generate a set of lyrics given arbitrary melodies. iComposer is based on three sequence-to-sequence models, which are used to predict melody, rhythm, and lyrics, respectively. Songs generated by iComposer are compared with human-composed and randomly-generated ones in a subjective test, the experimental results of which demonstrate the capability of the proposed system to write pleasing melodies and meaningful lyrics at a level similar to that of humans.

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GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
Tsu-Jui Fu | Peng-Hsuan Li | Wei-Yun Ma
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better extract relations. Linear and dependency structures are both used to extract both sequential and regional features of the text, and a complete word graph is further utilized to extract implicit features among all word pairs of the text. With the graph-based approach, the prediction for overlapping relations is substantially improved over previous sequential approaches. We evaluate GraphRel on two public datasets: NYT and WebNLG. Results show that GraphRel maintains high precision while increasing recall substantially. Also, GraphRel outperforms previous work by 3.2% and 5.8% (F1 score), achieving a new state-of-the-art for relation extraction.

2018

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Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese
Chi-Yen Chen | Wei-Yun Ma
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Extended HowNet 2.0 – An Entity-Relation Common-Sense Representation Model
Wei-Yun Ma | Yueh-Yin Shih
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Speed Reading: Learning to Read ForBackward via Shuttle
Tsu-Jui Fu | Wei-Yun Ma
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back. Shuttling forward enables high efficiency, and going backward gives the model a chance to recover lost information, ensuring better prediction. We evaluate LSTM-Shuttle on sentiment analysis, news classification, and cloze on IMDB, Rotten Tomatoes, AG, and Children’s Book Test datasets. We show that LSTM-Shuttle predicts both better and more quickly. To demonstrate how LSTM-Shuttle actually behaves, we also analyze the shuttling operation and present a case study.

2017

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Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Peng-Hsuan Li | Ruo-Ping Dong | Yu-Siang Wang | Ju-Chieh Chou | Wei-Yun Ma
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.

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Guess What: A Question Answering Game via On-demand Knowledge Validation
Yu-Sheng Li | Chien-Hui Tseng | Chian-Yun Huang | Wei-Yun Ma
Proceedings of the IJCNLP 2017, System Demonstrations

In this paper, we propose an idea of ondemand knowledge validation and fulfill the idea through an interactive Question-Answering (QA) game system, which is named Guess What. An object (e.g. dog) is first randomly chosen by the system, and then a user can repeatedly ask the system questions in natural language to guess what the object is. The system would respond with yes/no along with a confidence score. Some useful hints can also be given if needed. The proposed framework provides a pioneering example of on-demand knowledge validation in dialog environment to address such needs in AI agents/chatbots. Moreover, the released log data that the system gathered can be used to identify the most critical concepts/attributes of an existing knowledge base, which reflects human’s cognition about the world.

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CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases
Peng-Hsuan Li | Wei-Yun Ma | Hsin-Yang Wang
Proceedings of the IJCNLP 2017, Shared Tasks

CKIP takes part in solving the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP 2017. This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real values between 1 and 9. To achieve this, functions mapping Chinese character sequences to real numbers are built by regression techniques. In addition, the CKIP phrase Valence-Arousal (VA) predictor depends on knowledge of modifier words and head words. This includes the types of known modifier words, VA of head words, and distributional semantics of both these words. The predictor took the second place out of 13 teams on phrase VA prediction, with 0.444 MAE and 0.935 PCC on valence, and 0.395 MAE and 0.904 PCC on arousal.

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Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement
Hsin-Yang Wang | Wei-Yun Ma
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Distributional word representations are widely used in NLP tasks. These representations are based on an assumption that words with a similar context tend to have a similar meaning. To improve the quality of the context-based embeddings, many researches have explored how to make full use of existing lexical resources. In this paper, we argue that while we incorporate the prior knowledge with context-based embeddings, words with different occurrences should be treated differently. Therefore, we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings - different words would have different learning rates when adjusting their embeddings. In the result, we demonstrate that our embeddings get significant improvements on two different tasks: Word Similarity and Analogical Reasoning.

2016

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基於相依詞向量的剖析結果重估與排序(N-best Parse Rescoring Based on Dependency-Based Word Embeddings)
Yu-Ming Hsieh | Wei-Yun Ma
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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構建一個中文國小數學文字問題語料庫(Building a Corpus for Developing the Chinese Elementary School Math Word Problem Solver)[In Chinese]
Shen-Yun Miao | Su-Chu Lin | Wei-Yun Ma | Keh-Yih Su
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

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N-best Rescoring for Parsing Based on Dependency-Based Word Embeddings
Yu-Ming Hsieh | Wei-Yun Ma
International Journal of Computational Linguistics & Chinese Language Processing, Volume 21, Number 2, December 2016

2015

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Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation
Chien-Tsung Huang | Yi-Chung Lin | Chao-Chun Liang | Kuang-Yi Hsu | Shen-Yun Miao | Wei-Yun Ma | Lun-Wen Ku | Churn-Jung Liau | Keh-Yih Su
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation
Yi-Chung Lin | Chao-Chun Liang | Kuang-Yi Hsu | Chien-Tsung Huang | Shen-Yun Miao | Wei-Yun Ma | Lun-Wei Ku | Churn-Jung Liau | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 2, December 2015 - Special Issue on Selected Papers from ROCLING XXVII

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System Combination for Machine Translation through Paraphrasing
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2013

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Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Phrase-level System Combination for Machine Translation Based on Target-to-Target Decoding
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

In this paper, we propose a novel lattice-based MT combination methodology that we call Target-to-Target Decoding (TTD). The combination process is carried out as a “translation” from backbone to the combination result. This perspective suggests the use of existing phrase-based MT techniques in the combination framework. We show how phrase extraction rules and confidence estimations inspired from machine translation improve results. We also propose system-specific LMs for estimating N-gram consensus. Our results show that our approach yields a strong improvement over the best single MT system and competes with other state-of-the-art combination systems.

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Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
Wei-Yun Ma | Kathleen McKeown
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)

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Detecting and Correcting Syntactic Errors in Machine Translation Using Feature-Based Lexicalized Tree Adjoining Grammars
Wei-Yun Ma | Kathleen McKeown
International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 4, December 2012-Special Issue on Selected Papers from ROCLING XXIV

2011

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System Combination for Machine Translation Based on Text-to-Text Generation
Wei-Yun Ma | Kathleen Mckeown
Proceedings of Machine Translation Summit XIII: Papers

2009

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Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task
Kristen Parton | Kathleen R. McKeown | Bob Coyne | Mona T. Diab | Ralph Grishman | Dilek Hakkani-Tür | Mary Harper | Heng Ji | Wei Yun Ma | Adam Meyers | Sara Stolbach | Ang Sun | Gokhan Tur | Wei Xu | Sibel Yaman
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

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Where’s the Verb? Correcting Machine Translation During Question Answering
Wei-Yun Ma | Kathy McKeown
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2006

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中文動詞名物化判斷的統計式模型設計 (A Stochastic Model for Prediction of Deverbal Nouns in Mandarin Chinese) [In Chinese]
Wei-Yun Ma | Chu-Ren Huang
Proceedings of the 18th Conference on Computational Linguistics and Speech Processing

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Uniform and Effective Tagging of a Heterogeneous Giga-word Corpus
Wei-Yun Ma | Chu-Ren Huang
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Tagging as the most crucial annotation of language resources can still be challenging when the corpus size is big and when the corpus data is not homogeneous. The Chinese Gigaword Corpus is confounded by both challenges. The corpus containsroughly 1.12 billion Chinese characters from two heterogeneous sources: respective news in Taiwan and in Mainland China. In other words, in addition to its size, the data also contains two variants of Chinese that are known to exhibit substantial linguistic differences. We utilize Chinese Sketch Engine as the corpus query tool, by which grammar behaviours of the two heterogeneous resources could be captured and displayed in a unified web interface. In this paper, we report our answer to the two challenges to effectively tag this large-scale corpus. The evaluation result shows our mechanism of tagging maintains high annotation quality.

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Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar
Chu-Ren Huang | Wei-Yun Ma | Yi-Ching Wu | Chih-Ming Chiu
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

2003

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A Bottom-up Merging Algorithm for Chinese Unknown Word Extraction
Wei-Yun Ma | Keh-Jiann Chen
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

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Introduction to CKIP Chinese Word Segmentation System for the First International Chinese Word Segmentation Bakeoff
Wei-Yun Ma | Keh-Jiann Chen
Proceedings of the Second SIGHAN Workshop on Chinese Language Processing

2002

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Unknown Word Extraction for Chinese Documents
Keh-Jiann Chen | Wei-Yun Ma
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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中文語料庫構建及管理系統設計 (Design of Management System for Chinese Corpus Construction) [In Chinese]
Wei-Yun Ma | Yu-Ming Hsieh | Chang-Hua Yang | Keh-Jiann Chen
Proceedings of Research on Computational Linguistics Conference XIV