Doo Soon Kim


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Understanding Points of Correspondence between Sentences for Abstractive Summarization
Logan Lebanoff | John Muchovej | Franck Dernoncourt | Doo Soon Kim | Lidan Wang | Walter Chang | Fei Liu
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challenging for modern abstractive summarizers, substantially restricting their applicability in real-world scenarios. In this paper, we present an investigation into fusing sentences drawn from a document by introducing the notion of points of correspondence, which are cohesive devices that tie any two sentences together into a coherent text. The types of points of correspondence are delineated by text cohesion theory, covering pronominal and nominal referencing, repetition and beyond. We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. Our dataset bridges the gap between coreference resolution and summarization. It is publicly shared to serve as a basis for future work to measure the success of sentence fusion systems.

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Adjusting Image Attributes of Localized Regions with Low-level Dialogue
Tzu-Hsiang Lin | Alexander Rudnicky | Trung Bui | Doo Soon Kim | Jean Oh
Proceedings of the 12th Language Resources and Evaluation Conference

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions which require complex editing steps. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level command terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for users to choose from. Though compelled to express in low-level terms, user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. Analysis shows that users generally adapt to utilizing the proposed low-level language interface. We also identified object segmentation as the key factor to user satisfaction. Our work demonstrates advantages of low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.

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Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks
Seunghyun Yoon | Franck Dernoncourt | Doo Soon Kim | Trung Bui | Kyomin Jung
Proceedings of the 12th Language Resources and Evaluation Conference

In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation. First, we design a graph structure in which each node represents an individual sentence, and some pairs of nodes are selectively connected based on the text structure. Then, we develop an iterative attentive aggregation and a skip-combine method in which a node interacts with its neighborhood nodes to accumulate the necessary information. To evaluate the performance of the proposed approaches, we conduct experiments with the standard HotpotQA dataset. The empirical results demonstrate the superiority of our proposed approach, which obtains the best performances, compared to the widely used answer-selection models that do not consider the intersentential relationship.

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TutorialVQA: Question Answering Dataset for Tutorial Videos
Anthony Colas | Seokhwan Kim | Franck Dernoncourt | Siddhesh Gupte | Zhe Wang | Doo Soon Kim
Proceedings of the 12th Language Resources and Evaluation Conference

Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.

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Efficient Deployment of Conversational Natural Language Interfaces over Databases
Anthony Colas | Trung Bui | Franck Dernoncourt | Moumita Sinha | Doo Soon Kim
Proceedings of the First Workshop on Natural Language Interfaces

Many users communicate with chatbots and AI assistants in order to help them with various tasks. A key component of the assistant is the ability to understand and answer a user’s natural language questions for question-answering (QA). Because data can be usually stored in a structured manner, an essential step involves turning a natural language question into its corresponding query language. However, in order to train most natural language-to-query-language state-of-the-art models, a large amount of training data is needed first. In most domains, this data is not available and collecting such datasets for various domains can be tedious and time-consuming. In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session, enabling one to better utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL models, on both an SQL and SPARQL-based datasets in order to showcase the adaptability and efficacy of our created data.

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A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents
Tuan Lai | Trung Bui | Doo Soon Kim | Quan Hung Tran
Proceedings of the 28th International Conference on Computational Linguistics

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.

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Learning to Fuse Sentences with Transformers for Summarization
Logan Lebanoff | Franck Dernoncourt | Doo Soon Kim | Lidan Wang | Walter Chang | Fei Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts. However, to date, summarizers can fail on fusing sentences. They tend to produce few summary sentences by fusion or generate incorrect fusions that lead the summary to fail to retain the original meaning. In this paper, we explore the ability of Transformers to fuse sentences and propose novel algorithms to enhance their ability to perform sentence fusion by leveraging the knowledge of points of correspondence between sentences. Through extensive experiments, we investigate the effects of different design choices on Transformer’s performance. Our findings highlight the importance of modeling points of correspondence between sentences for effective sentence fusion.

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ViLBERTScore: Evaluating Image Caption Using Vision-and-Language BERT
Hwanhee Lee | Seunghyun Yoon | Franck Dernoncourt | Doo Soon Kim | Trung Bui | Kyomin Jung
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems

In this paper, we propose an evaluation metric for image captioning systems using both image and text information. Unlike the previous methods that rely on textual representations in evaluating the caption, our approach uses visiolinguistic representations. The proposed method generates image-conditioned embeddings for each token using ViLBERT from both generated and reference texts. Then, these contextual embeddings from each of the two sentence-pair are compared to compute the similarity score. Experimental results on three benchmark datasets show that our method correlates significantly better with human judgments than all existing metrics.


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Analyzing Sentence Fusion in Abstractive Summarization
Logan Lebanoff | John Muchovej | Franck Dernoncourt | Doo Soon Kim | Seokhwan Kim | Walter Chang | Fei Liu
Proceedings of the 2nd Workshop on New Frontiers in Summarization

While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences. In this paper, we analyze the outputs of five state-of-the-art abstractive summarizers, focusing on summary sentences that are formed by sentence fusion. We ask assessors to judge the grammaticality, faithfulness, and method of fusion for summary sentences. Our analysis reveals that system sentences are mostly grammatical, but often fail to remain faithful to the original article.

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Scoring Sentence Singletons and Pairs for Abstractive Summarization
Logan Lebanoff | Kaiqiang Song | Franck Dernoncourt | Doo Soon Kim | Seokhwan Kim | Walter Chang | Fei Liu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.


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PhotoshopQuiA: A Corpus of Non-Factoid Questions and Answers for Why-Question Answering
Andrei Dulceanu | Thang Le Dinh | Walter Chang | Trung Bui | Doo Soon Kim | Manh Chien Vu | Seokhwan Kim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing
Ramesh Manuvinakurike | Jacqueline Brixey | Trung Bui | Walter Chang | Doo Soon Kim | Ron Artstein | Kallirroi Georgila
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Arman Cohan | Franck Dernoncourt | Doo Soon Kim | Trung Bui | Seokhwan Kim | Walter Chang | Nazli Goharian
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.


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Building a Lightweight Semantic Model for Unsupervised Information Extraction on Short Listings
Doo Soon Kim | Kunal Verma | Peter Yeh
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


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Building an end-to-end text reading system based on a packed representation
Doo Soon Kim | Ken Barker | Bruce Porter
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading

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Improving the Quality of Text Understanding by Delaying Ambiguity Resolution
Doo Soon Kim | Ken Barker | Bruce Porter
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)