Ting-Hao Huang

Also published as: Ting-Hao Kenneth Huang, Ting-Hao ‘Kenneth’ Huang


2021

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Semantic Frame Forecast
Chieh-Yang Huang | Ting-Hao Huang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of “story blocks,” where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame’s inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the feasibility of the task. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.

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ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
Yanjun Gao | Ting-Hao Huang | Rebecca J. Passonneau
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.

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Stretch-VST: Getting Flexible With Visual Stories
Chi-yang Hsu | Yun-Wei Chu | Tsai-Lun Yang | Ting-Hao Huang | Lun-Wei Ku
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

In visual storytelling, a short story is generated based on a given image sequence. Despite years of work, most visual storytelling models remain limited in terms of the generated stories’ fixed length: most models produce stories with exactly five sentences because five-sentence stories dominate the training data. The fix-length stories carry limited details and provide ambiguous textual information to the readers. Therefore, we propose to “stretch” the stories, which create the potential to present in-depth visual details. This paper presents Stretch-VST, a visual storytelling framework that enables the generation of prolonged stories by adding appropriate knowledge, which is selected by the proposed scoring function. We propose a length-controlled Transformer to generate long stories. This model introduces novel positional encoding methods to maintain story quality with lengthy inputs. Experiments confirm that long stories are generated without deteriorating the quality. The human evaluation further shows that Stretch-VST can provide better focus and detail when stories are prolonged compared to state of the art. We create a webpage to demonstrate our prolonged capability.

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Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction
Yanjun Gao | Ting-Hao Huang | Rebecca J. Passonneau
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We propose a novel clause embedding method that applies graph learning to a data structure we refer to as a dependency-anchor graph. The dependency anchor graph incorporates two kinds of syntactic information, constituency structure, and dependency relations, to highlight the subject and verb phrase relation. This enhances coherence-related aspects of representation. We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase. We evaluate our method on two new datasets: a subset of a large corpus where the source texts are published novels, and a new dataset collected from students’ essays. The results demonstrate a significant improvement over tree-based models, confirming the importance of emphasizing the subject and verb phrase. The performance gap between the two datasets illustrates the challenges of analyzing student’s written text, plus a potential evaluation task for coherence modeling and an application for suggesting revisions to students.

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Plot and Rework: Modeling Storylines for Visual Storytelling
Chi-yang Hsu | Yun-Wei Chu | Ting-Hao Huang | Lun-Wei Ku
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Assessing the Helpfulness of Learning Materials with Inference-Based Learner-Like Agent
Yun-Hsuan Jen | Chieh-Yang Huang | MeiHua Chen | Ting-Hao Huang | Lun-Wei Ku
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Many English-as-a-second language learners have trouble using near-synonym words (e.g., small vs.little; briefly vs.shortly) correctly, and often look for example sentences to learn how two nearly synonymous terms differ. Prior work uses hand-crafted scores to recommend sentences but has difficulty in adopting such scores to all the near-synonyms as near-synonyms differ in various ways. We notice that the helpfulness of the learning material would reflect on the learners’ performance. Thus, we propose the inference-based learner-like agent to mimic learner behavior and identify good learning materials by examining the agent’s performance. To enable the agent to behave like a learner, we leverage entailment modeling’s capability of inferring answers from the provided materials. Experimental results show that the proposed agent is equipped with good learner-like behavior to achieve the best performance in both fill-in-the-blank (FITB) and good example sentence selection tasks. We further conduct a classroom user study with college ESL learners. The results of the user study show that the proposed agent can find out example sentences that help students learn more easily and efficiently. Compared to other models, the proposed agent improves the score of more than 17% of students after learning.

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CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
Ting-Hao Kenneth Huang | Chieh-Yang Huang | Chien-Kuang Cornelia Ding | Yen-Chia Hsu | C. Lee Giles
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10,966 English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by 248 crowd workers from Amazon Mechanical Turk within 10 days, and achieved labeling quality comparable to that of experts. Each abstract was annotated by nine different workers, and the final labels were acquired by majority vote. The inter-annotator agreement (Cohen’s kappa) between the crowd and the biomedical expert (0.741) is comparable to inter-expert agreement (0.788). CODA-19’s labels have an accuracy of 82.2% when compared to the biomedical expert’s labels, while the accuracy between experts was 85.0%. Reliable human annotations help scientists access and integrate the rapidly accelerating coronavirus literature, and also serve as the battery of AI/NLP research, but obtaining expert annotations can be slow. We demonstrated that a non-expert crowd can be rapidly employed at scale to join the fight against COVID-19.

2019

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Proceedings of the Second Workshop on Storytelling
Francis Ferraro | Ting-Hao ‘Kenneth’ Huang | Stephanie M. Lukin | Margaret Mitchell
Proceedings of the Second Workshop on Storytelling

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Visual Story Post-Editing
Ting-Yao Hsu | Chieh-Yang Huang | Yen-Chia Hsu | Ting-Hao Huang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task. The dataset ,VIST-Edit, includes 14,905 human-edited versions of 2,981 machine-generated visual stories. The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions. We establish baselines for the task, showing how a relatively small set of human edits can be leveraged to boost the performance of large visual storytelling models. We also discuss the weak correlation between automatic evaluation scores and human ratings, motivating the need for new automatic metrics.

2018

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EmotionLines: An Emotion Corpus of Multi-Party Conversations
Chao-Chun Hsu | Sheng-Yeh Chen | Chuan-Chun Kuo | Ting-Hao Huang | Lun-Wei Ku
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Proceedings of the First Workshop on Storytelling
Margaret Mitchell | Ting-Hao ‘Kenneth’ Huang | Francis Ferraro | Ishan Misra
Proceedings of the First Workshop on Storytelling

2017

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MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions
Chieh-Yang Huang | Tristan Labetoulle | Ting-Hao Huang | Yi-Pei Chen | Hung-Chen Chen | Vallari Srivastava | Lun-Wei Ku
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.

2016

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Visual Storytelling
Ting-Hao Kenneth Huang | Francis Ferraro | Nasrin Mostafazadeh | Ishan Misra | Aishwarya Agrawal | Jacob Devlin | Ross Girshick | Xiaodong He | Pushmeet Kohli | Dhruv Batra | C. Lawrence Zitnick | Devi Parikh | Lucy Vanderwende | Michel Galley | Margaret Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Sensing Emotions in Text Messages: An Application and Deployment Study of EmotionPush
Shih-Ming Wang | Chun-Hui Scott Lee | Yu-Chun Lo | Ting-Hao Huang | Lun-Wei Ku
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Instant messaging and push notifications play important roles in modern digital life. To enable robust sense-making and rich context awareness in computer mediated communications, we introduce EmotionPush, a system that automatically conveys the emotion of received text with a colored push notification on mobile devices. EmotionPush is powered by state-of-the-art emotion classifiers and is deployed for Facebook Messenger clients on Android. The study showed that the system is able to help users prioritize interactions.

2015

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A Survey of Current Datasets for Vision and Language Research
Francis Ferraro | Nasrin Mostafazadeh | Ting-Hao Huang | Lucy Vanderwende | Jacob Devlin | Michel Galley | Margaret Mitchell
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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ACBiMA: Advanced Chinese Bi-Character Word Morphological Analyzer
Ting-Hao Huang | Yun-Nung Chen | Lingpeng Kong
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

2014

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Social Metaphor Detection via Topical Analysis
Ting-Hao Kenneth Huang
International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 2, June 2014

2013

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Social Metaphor Detection via Topical Analysis
Ting-Hao Huang
Proceedings of the IJCNLP 2013 Workshop on Natural Language Processing for Social Media (SocialNLP)

2012

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Modeling Pollyanna Phenomena in Chinese Sentiment Analysis
Ting-Hao Huang | Ho-Cheng Yu | Hsin-Hsi Chen
Proceedings of COLING 2012: Demonstration Papers

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領域相關詞彙極性分析及文件情緒分類之研究 (Domain Dependent Word Polarity Analysis for Sentiment Classification) [In Chinese]
Ho-Cheng Yu | Ting-Hao Huang | Hsin-Hsi Chen
Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012)

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領域相關詞彙極性分析及文件情緒分類之研究 (Domain Dependent Word Polarity Analysis for Sentiment Classification) [In Chinese]
Ho-Cheng Yu | Ting-Hao Kenneth Huang | Hsin-Hsi Chen
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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Predicting Opinion Dependency Relations for Opinion Analysis
Lun-Wei Ku | Ting-Hao Huang | Hsin-Hsi Chen
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Construction of a Chinese Opinion Treebank
Lun-Wei Ku | Ting-Hao Huang | Hsin-Hsi Chen
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper, we base on the syntactic structural Chinese Treebank corpus, construct the Chinese Opinon Treebank for the research of opinion analysis. We introduce the tagging scheme and develop a tagging tool for constructing this corpus. Annotated samples are described. Information including opinions (yes or no), their polarities (positive, neutral or negative), types (expression, status, or action), is defined and annotated. In addition, five structure trios are introduced according to the linguistic relations between two Chinese words. Four of them that are possibly related to opinions are also annotated in the constructed corpus to provide the linguistic cues. The number of opinion sentences together with the number of their polarities, opinion types, and trio types are calculated. These statistics are compared and discussed. To know the quality of the annotations in this corpus, the kappa values of the annotations are calculated. The substantial agreement between annotations ensures the applicability and reliability of the constructed corpus.

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Predicting Morphological Types of Chinese Bi-Character Words by Machine Learning Approaches
Ting-Hao Huang | Lun-Wei Ku | Hsin-Hsi Chen
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presented an overview of Chinese bi-character words’ morphological types, and proposed a set of features for machine learning approaches to predict these types based on composite characters’ information. First, eight morphological types were defined, and 6,500 Chinese bi-character words were annotated with these types. After pre-processing, 6,178 words were selected to construct a corpus named Reduced Set. We analyzed Reduced Set and conducted the inter-annotator agreement test. The average kappa value of 0.67 indicates a substantial agreement. Second, Bi-character words’ morphological types are considered strongly related with the composite characters’ parts of speech in this paper, so we proposed a set of features which can simply be extracted from dictionaries to indicate the characters’ “tendency” of parts of speech. Finally, we used these features and adopted three machine learning algorithms, SVM, CRF, and Naïve Bayes, to predict the morphological types. On the average, the best algorithm CRF achieved 75% of the annotators’ performance.

2009

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Using Morphological and Syntactic Structures for Chinese Opinion Analysis
Lun-Wei Ku | Ting-Hao Huang | Hsin-Hsi Chen
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing