Papers by Keh-Yih Su

5 papers
A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020.acl-main)

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Challenge: Existing MWP corpora are limited in language patterns and problem types . a new corpus of 2,305 MWps is proposed that is more diverse in terms of lexicon usage .
Approach: They propose to use ASDiv to measure lexicon usage diversity of a given MWP corpus.
Outcome: The proposed corpus covers more problem types and text patterns than existing corpora and reflects the true capability of solvers more faithfully.
A Meaning-Based Statistical English Math Word Problem Solver (N18-1)

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Challenge: Experimental results show that the proposed approach understands the meaning of each quantity in the text more.
Approach: They propose a meaning-based approach for solving English math word problems . they analyze text, transform body and question parts into corresponding logic forms . Statistical models are proposed to select operator and operands .
Outcome: The proposed approach outperforms existing systems on benchmark and noisy datasets.
How Fast can BERT Learn Simple Natural Language Inference? (2021.eacl-main)

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Challenge: Efficiency of learning of BERT is very slow due to hidden dataset bias . however, some studies show that it can learn with surface clues/patterns .
Approach: They propose to use a simple entailment judgment case to test whether BERT can learn without hidden dataset bias.
Outcome: The proposed case shows that BERT can learn without hidden bias without utilizing dataset bias.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference (C18-1)

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Challenge: Existing approaches to perform natural language inference ignore syntactic dependency among words or use tree-LSTM to generate sentence representation with irrelevant information.
Approach: They propose to perform natural language inference with Word-Pair-Dependency-Triplets . they propose to compare the triplets of a given passage-pair to make judgement more interpretable .
Outcome: The proposed approach is better than most of the approaches that use tree structures and comparable to other state-of-the-art approaches.
Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving (2021.acl-short)

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Challenge: Existing neural solvers only generate binary expression trees that contain basic arithmetic operators and do not explicitly use the math formulas.
Approach: They propose a sequence-to-general tree that generates interpretable and executable operation trees where nodes can be formulas with an arbitrary number of arguments.
Outcome: The proposed tree generates interpretable and executable operation trees with formulas with an arbitrary number of arguments.

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