pwc-archive/papers-with-abstracts huggingface dataset:
Cross-lingual Transfer Learning for Japanese Named Entity Recognition
Authors: Andrew Johnson, Penny Karanasou, Judith Gaspers, Dietrich Klakow
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Cross-Lingual Transfer, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

This work explores cross-lingual transfer learning (TL) for named entity recognition, focusing on bootstrapping Japanese from English. A deep neural network model is adopted and the best combination of weights to transfer is extensively investigated. Moreover, a novel approach is presented that overcomes linguistic differences between this language pair by romanizing a portion of the Japanese input. Experiments are conducted on external datasets, as well as internal large-scale real-world ones. Gains with TL are achieved for all evaluated cases. Finally, the influence on TL of the target dataset size and of the target tagset distribution is further investigated.
Neural Text Normalization with Subword Units
Authors: Courtney Mansfield, Ming Sun, Yuzong Liu, G, Ankur he, Bj{\"o}rn Hoffmeister
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Natural Language Understanding, speech-recognition, Speech Recognition, Speech Synthesis, Text Normalization, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Translation

Text normalization (TN) is an important step in conversational systems. It converts written text to its spoken form to facilitate speech recognition, natural language understanding and text-to-speech synthesis. Finite state transducers (FSTs) are commonly used to build grammars that handle text normalization. However, translating linguistic knowledge into grammars requires extensive effort. In this paper, we frame TN as a machine translation task and tackle it with sequence-to-sequence (seq2seq) models. Previous research focuses on normalizing a word (or phrase) with the help of limited word-level context, while our approach directly normalizes full sentences. We find subword models with additional linguistic features yield the best performance (with a word error rate of 0.17{\%}).
Audio De-identification - a New Entity Recognition Task
Authors: Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), De-identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, speech-recognition, Speech Recognition

Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline{'}s results on it.
Content-based Dwell Time Engagement Prediction Model for News Articles
Authors: Heidar Davoudi, Aijun An, Gordon Edall
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Articles

The article dwell time (i.e., expected time that users spend on an article) is among the most important factors showing the article engagement. It is of great interest to predict the dwell time of an article before its release. This allows digital newspapers to make informed decisions and publish more engaging articles. In this paper, we propose a novel content-based approach based on a deep neural network architecture for predicting article dwell times. The proposed model extracts emotion, event and entity features from an article, learns interactions among them, and combines the interactions with the word-based features of the article to learn a model for predicting the dwell time. The experimental results on a real dataset from a major newspaper show that the proposed model outperforms other state-of-the-art baselines.
Is It Dish Washer Safe? Automatically Answering ``Yes/No'' Questions Using Customer Reviews
Authors: Daria Dzendzik, Carl Vogel, Jennifer Foster
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering, Reading Comprehension

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{\%}.
Emotion Impacts Speech Recognition Performance
Authors: Rushab Munot, Ani Nenkova
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: speech-recognition, Speech Recognition

It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect. Here we use three English datasets of acted emotion to demonstrate that emotional content also impacts the performance of commercial systems. On two of the corpora, emotion is a bigger contributor to recognition errors than speaker identity and on two, neutral speech is recognized considerably better than emotional speech. We further evaluate the commercial systems on spontaneous interactions that contain portions of emotional speech. We propose and validate on the acted datasets, a method that allows us to evaluate the overall impact of emotion on recognition even when manual transcripts are not available. Using this method, we show that emotion in natural spontaneous dialogue is a less prominent but still significant factor in recognition accuracy.
The Strength of the Weakest Supervision: Topic Classification Using Class Labels
Authors: Jiatong Li, Kai Zheng, Hua Xu, Qiaozhu Mei, Yue Wang
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, Topic Classification

When developing topic classifiers for real-world applications, we begin by defining a set of meaningful topic labels. Ideally, an intelligent classifier can understand these labels right away and start classifying documents. Indeed, a human can confidently tell if an article is about science, politics, sports, or none of the above, after knowing just the class labels. We study the problem of training an initial topic classifier using only class labels. We investigate existing techniques for solving this problem and propose a simple but effective approach. Experiments on a variety of topic classification data sets show that learning from class labels can save significant initial labeling effort, essentially providing a {''}free{''} warm start to the topic classifier.
Opinion Mining with Deep Contextualized Embeddings
Authors: Wen-Bin Han, K, Noriko o
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Opinion Mining, Word Embeddings

Detecting opinion expression is a potential and essential task in opinion mining that can be extended to advanced tasks. In this paper, we considered opinion expression detection as a sequence labeling task and exploited different deep contextualized embedders into the state-of-the-art architecture, composed of bidirectional long short-term memory (BiLSTM) and conditional random field (CRF). Our experimental results show that using different word embeddings can cause contrasting results, and the model can achieve remarkable scores with deep contextualized embeddings. Especially, using BERT embedder can significantly exceed using ELMo embedder.
A Partially Rule-Based Approach to AMR Generation
Authors: Emma Manning
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstract Meaning Representation, Language Modeling, Language Modelling

This paper presents a new approach to generating English text from Abstract Meaning Representation (AMR). In contrast to the neural and statistical MT approaches used in other AMR generation systems, this one is largely rule-based, supplemented only by a language model and simple statistical linearization models, allowing for more control over the output. We also address the difficulties of automatically evaluating AMR generation systems and the problems with BLEU for this task. We compare automatic metrics to human evaluations and show that while METEOR and TER arguably reflect human judgments better than BLEU, further research into suitable evaluation metrics is needed.
Computational Investigations of Pragmatic Effects in Natural Language
Authors: Jad Kabbara
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Text Generation

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.
SEDTWik: Segmentation-based Event Detection from Tweets Using Wikipedia
Authors: Keval Morabia, Neti Lalita Bhanu Murthy, Aruna Malapati, Surender Samant
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Event Detection, TAG, Twitter Event Detection

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.
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks
Authors: Sarah Gupta, Anthony DiPadova
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

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.
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding
Authors: Hwa-Yeon Kim, Yoon-Hyung Roh, Young-Kil Kim
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Data Augmentation, slot-filling, Slot Filling, Spoken Language Understanding

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.
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech
Authors: Samvit Dammalapati, Rajakrishnan Rajkumar, Sumeet Agarwal
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

This study examines the role of three influential theories of language processing, \textit{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.
A Pregroup Representation of Word Order Alternation Using Hindi Syntax
Authors: Alok Debnath, Manish Shrivastava
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

Pregroup calculus has been used for the representation of free word order languages (Sanskrit and Hungarian), using a construction called precyclicity. However, restricted word order alternation has not been handled before. This paper aims at introducing and formally expressing three methods of representing word order alternation in the pregroup representation of any language. This paper describes the word order alternation patterns of Hindi, and creates a basic pregroup representation for the language. In doing so, the shortcoming of correct reductions for ungrammatical sentences due to the current apparatus is highlighted, and the aforementioned methods are invoked for a grammatically accurate representation of restricted word order alternation. The replicability of these methods is explained in the representation of adverbs and prepositional phrases in English.
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment
Authors: Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Sentence, Sentence Classification, Specificity

The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment. The aggregation and analysis of such disclosures pave the way to development of technology-based prevention of sexual harassment. We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment. We propose the Disclosure Language Model, a three part ULMFiT architecture, consisting of a Language model, a Medium-Specific (Twitter) model and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this, to show that using a Discourse Language Model often yields better classification performance over (i) Generic deep learning based sentence classification models (ii) existing models that rely on handcrafted stylistic features. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology.
SNAP-BATNET: Cascading Author Profiling and Social Network Graphs for Suicide Ideation Detection on Social Media
Authors: Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Author Profiling

Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research. While it has been established that these users share a common set of properties, the current state-of-the-art approaches utilize only text-based (stylistic and semantic) cues. We contend that the use of information from networks in the form of condensed social graph embeddings and author profiling using features from historical data can be combined with an existing set of features to improve the performance. To that end, we experiment on a manually annotated dataset of tweets created using a three-phase strategy and propose SNAP-BATNET, a deep learning based model to extract text-based features and a novel Feature Stacking approach to combine other community-based information such as historical author profiling and graph embeddings that outperform the current state-of-the-art. We conduct a comprehensive quantitative analysis with baselines, both generic and specific, that presents the case for SNAP-BATNET, along with an error analysis that highlights the limitations and challenges faced paving the way to the future of AI-based suicide ideation detection.
Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation
Authors: Manuel R. Ciosici, Ira Assent
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

We present Abbreviation Explorer, a system that supports interactive exploration of abbreviations that are challenging for Unsupervised Abbreviation Disambiguation (UAD). Abbreviation Explorer helps to identify long-forms that are easily confused, and to pinpoint likely causes such as limitations of normalization, language switching, or inconsistent typing. It can also support determining which long-forms would benefit from additional input text for unsupervised abbreviation disambiguation. The system provides options for creating corrective rules that merge redundant long-forms with identical meaning. The identified rules can be easily applied to the already existing vector spaces used by UAD to improve disambiguation performance, while also avoiding the cost of retraining.
ADIDA: Automatic Dialect Identification for Arabic
Authors: Ossama Obeid, Mohammad Salameh, Houda Bouamor, Nizar Habash
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dialect Identification

This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text. The system distinguishes among the dialects of 25 Arab cities (from Rabat to Muscat) in addition to Modern Standard Arabic. The results are presented with either a point map or a heat map visualizing the automatic identification probabilities over a geographical map of the Arab World.
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
Authors: George C. G. Barbosa, Zechy Wong, Gus Hahn-Powell, Dane Bell, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Mihai Surdeanu
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6

Many of the most pressing current research problems (e.g., public health, food security, or climate change) require multi-disciplinary collaborations. In order to facilitate this process, we propose a system that incorporates multi-domain extractions of causal interactions into a single searchable knowledge graph. Our system enables users to search iteratively over direct and indirect connections in this knowledge graph, and collaboratively build causal models in real time. To enable the aggregation of causal information from multiple languages, we extend an open-domain machine reader to Portuguese. The new Portuguese reader extracts over 600 thousand causal statements from 120 thousand Portuguese publications with a precision of 62{\%}, which demonstrates the value of mining multilingual scientific information.
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings
Authors: Chia-Fang Ho, Jason Chang, Jhih-Jie Chen, Ching-Yu Yang
Date: 2019-06-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Sentence, Translation, Word Embeddings, Word Translation

We present a method for learning bilingual word embeddings in order to support second language (L2) learners in finding recurring phrases and example sentences that match mixed-code queries (e.g., {``}接 受 sentence{''}) composed of words in both target language and native language (L1). In our approach, mixed-code queries are transformed into target language queries aimed at maximizing the probability of retrieving relevant target language phrases and sentences. The method involves converting a given parallel corpus into mixed-code data, generating word embeddings from mixed-code data, and expanding queries in target languages based on bilingual word embeddings. We present a prototype search engine, x.Linggle, that applies the method to a linguistic search engine for a parallel corpus. Preliminary evaluation on a list of common word-translation shows that the method performs reasonablly well.