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Ting-Hao Huang

7 accepted papers

2022

Are Shortest Rationales the Best Explanations for Human Understanding?

ACL 2022short

Existing self-explaining models typically favor extracting the shortest possible rationales — snippets of an input text “responsible for” corresponding output — to explain the model prediction, with the assumption that shorter rationales are more intuitive to humans. However, this assumption has yet…

2022

Learning to Rank Visual Stories From Human Ranking Data

ACL 2022long

Visual storytelling (VIST) is a typical vision and language task that has seen extensive development in the natural language generation research domain. However, it remains unclear whether conventional automatic evaluation metrics for text generation are applicable on VIST. In this paper, we present…

2022

Multi-VQG: Generating Engaging Questions for Multiple Images

EMNLP 2022main

Generating engaging content has drawn much recent attention in the NLP community. Asking questions is a natural way to respond to photos and promote awareness. However, most answers to questions in traditional question-answering (QA) datasets are factoids, which reduce individuals’ willingness to an…

2021

ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

ACL 2021long

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…

2021

FinQA: A Dataset of Numerical Reasoning over Financial Data

EMNLP 2021main

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…