Ting-Hao Huang

Also published as: Ting-Hao ‘Kenneth’ Huang


2021

pdf bib
ABCD : A Graph Framework to Convert Complex Sentences to a Covering Set of Simple SentencesABCD: 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.

pdf bib
FinQA : A Dataset of Numerical Reasoning over Financial DataFinQA: A Dataset of Numerical Reasoning over Financial Data
Zhiyu Chen | Wenhu Chen | Charese Smiley | Sameena Shah | Iana Borova | Dylan Langdon | Reema Moussa | Matt Beane | Ting-Hao Huang | Bryan Routledge | William Yang Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset the first of its kind should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available at https://github.com/czyssrs/FinQA.

2019

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
MoodSwipe : A Soft Keyboard that Suggests MessageBased on User-Specified EmotionsMoodSwipe: 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.