Seungtaek Choi


2022

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Debiasing Event Understanding for Visual Commonsense Tasks
Minji Seo | YeonJoon Jung | Seungtaek Choi | Seung-won Hwang | Bei Liu
Findings of the Association for Computational Linguistics: ACL 2022

We study event understanding as a critical step towards visual commonsense tasks.Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects.We propose to mitigate such biases with do-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction.We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.

2021

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Structure-Augmented Keyphrase Generation
Jihyuk Kim | Myeongho Jeong | Seungtaek Choi | Seung-won Hwang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. We propose novel structure-augmented document encoding approaches that consist of the following two phases: The first phase, generating structure, extends the given document with related but absent keyphrases, augmenting missing context. The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text. Our empirical results validate that our proposed structure augmentation and augmentation-aware encoding/decoding can improve KG for both scenarios, outperforming the state-of-the-art.

2020

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Retrieval-Augmented Controllable Review Generation
Jihyeok Kim | Seungtaek Choi | Reinald Kim Amplayo | Seung-won Hwang
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating. This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text. The capacity of these models is thus confined and dependent to how well the models can capture vector representations of attributes. We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes. With these references, we can now pose the problem as an instance of text-to-text generation, which makes the task easier since texts that are syntactically, semantically similar with the output text are provided as input. Using this framework, we address issues such as selecting references from a large candidate set without textual context and improving the model complexity for generation. Our experiments show that our models improve over previous approaches on both automatic and human evaluation metrics.

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Less is More: Attention Supervision with Counterfactuals for Text Classification
Seungtaek Choi | Haeju Park | Jinyoung Yeo | Seung-won Hwang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We aim to leverage human and machine intelligence together for attention supervision. Specifically, we show that human annotation cost can be kept reasonably low, while its quality can be enhanced by machine self-supervision. Specifically, for this goal, we explore the advantage of counterfactual reasoning, over associative reasoning typically used in attention supervision. Our empirical results show that this machine-augmented human attention supervision is more effective than existing methods requiring a higher annotation cost, in text classification tasks, including sentiment analysis and news categorization.

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Label-Efficient Training for Next Response Selection
Seungtaek Choi | Myeongho Jeong | Jinyoung Yeo | Seung-won Hwang
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.

2019

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MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering
Hojae Han | Seungtaek Choi | Haeju Park | Seung-won Hwang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences. Existing solutions can be categorized into representation- and interaction-focused approaches. We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. Our contributions are as follows: First, we enable multi-granular matches between question and answer n-grams. Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval. We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.

2018

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Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning
Jinyoung Yeo | Gyeongbok Lee | Gengyu Wang | Seungtaek Choi | Hyunsouk Cho | Reinald Kim Amplayo | Seung-won Hwang
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)