Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Sudipta Kar, Farah Nadeem, Laura Burdick, Greg Durrett, Na-Rae Han (Editors)


Anthology ID:
N19-3
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
URL:
https://aclanthology.org/N19-3
DOI:
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https://aclanthology.org/N19-3.pdf

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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Sudipta Kar | Farah Nadeem | Laura Burdick | Greg Durrett | Na-Rae Han

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Is It Dish Washer Safe? Automatically Answering Yes / No Questions Using Customer Reviews
Daria Dzendzik | Carl Vogel | Jennifer Foster

It has become commonplace for people to share their opinions about all kinds of products by posting reviews online. It has also become commonplace for potential customers to do research about the quality and limitations of these products by posting questions online. We test the extent to which reviews are useful in question-answering by combining two Amazon datasets and focusing our attention on yes / no questions. A manual analysis of 400 cases reveals that the reviews directly contain the answer to the question just over a third of the time. Preliminary reading comprehension experiments with this dataset prove inconclusive, with accuracy in the range 50-66 %.

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Identifying and Reducing Gender Bias in Word-Level Language Models
Shikha Bordia | Samuel R. Bowman

Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias ; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus ; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender ; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corporaPenn Treebank, WikiText-2, and CNN / Daily Mailresulting in similar conclusions.

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Computational Investigations of Pragmatic Effects in Natural Language
Jad Kabbara

Semantics and pragmatics are two complimentary and intertwined aspects of meaning in language. The former is concerned with the literal (context-free) meaning of words and sentences, the latter focuses on the intended meaning, one that is context-dependent. While NLP research has focused in the past mostly on semantics, the goal of this thesis is to develop computational models that leverage this pragmatic knowledge in language that is crucial to performing many NLP tasks correctly. In this proposal, we begin by reviewing the current progress in this thesis, namely, on the tasks of definiteness prediction and adverbial presupposition triggering. Then we discuss the proposed research for the remainder of the thesis which builds on this progress towards the goal of building better and more pragmatically-aware natural language generation and understanding systems.

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SEDTWik : Segmentation-based Event Detection from Tweets Using WikipediaSEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia
Keval Morabia | Neti Lalita Bhanu Murthy | Aruna Malapati | Surender Samant

Event Detection has been one of the research areas in Text Mining that has attracted attention during this decade due to the widespread availability of social media data specifically twitter data. Twitter has become a major source for information about real-world events because of the use of hashtags and the small word limit of Twitter that ensures concise presentation of events. Previous works on event detection from tweets are either applicable to detect localized events or breaking news only or miss out on many important events. This paper presents the problems associated with event detection from tweets and a tweet-segmentation based system for event detection called SEDTWik, an extension to a previous work, that is able to detect newsworthy events occurring at different locations of the world from a wide range of categories. The main idea is to split each tweet and hash-tag into segments, extract bursty segments, cluster them, and summarize them. We evaluated our results on the well-known Events2012 corpus and achieved state-of-the-art results. Keywords : Event detection, Twitter, Social Media, Microblogging, Tweet segmentation, Text Mining, Wikipedia, Hashtag.

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Multimodal Machine Translation with Embedding Prediction
Tosho Hirasawa | Hayahide Yamagishi | Yukio Matsumura | Mamoru Komachi

Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.

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Deep Learning and Sociophonetics : Automatic Coding of Rhoticity Using Neural Networks
Sarah Gupta | Anthony DiPadova

Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks / Deep Learning, training our model on 208 Boston-area speakers.

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Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding
Hwa-Yeon Kim | Yoon-Hyung Roh | Young-Kil Kim

One of the main challenges in Spoken Language Understanding (SLU) is dealing with ‘open-vocabulary’ slots. Recently, SLU models based on neural network were proposed, but it is still difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. This paper proposes data noising, which reflects the characteristics of the ‘open-vocabulary’ slots, for data augmentation. We applied it to an attention based bi-directional recurrent neural network (Liu and Lane, 2016) and experimented with three datasets : Airline Travel Information System (ATIS), Snips, and MIT-Restaurant. We achieved performance improvements of up to 0.57 % and 3.25 in intent prediction (accuracy) and slot filling (f1-score), respectively. Our method is advantageous because it does not require additional memory and it can be applied simultaneously with the training process of the model.

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Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English SpeechEnglish Speech
Samvit Dammalapati | Rajakrishnan Rajkumar | Sumeet Agarwal

This study examines the role of three influential theories of language processing, viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.