pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Variability Measure for Multiword Expressions
Authors: Caroline Pasquer, Agata Savary, Jean-Yves Antoine, Carlos Ramisch
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

One of the most outstanding properties of multiword expressions (MWEs), especially verbal ones (VMWEs), important both in theoretical models and applications, is their idiosyncratic variability. Some MWEs are always continuous, while some others admit certain types of insertions. Components of some MWEs are rarely or never modified, while some others admit either specific or unrestricted modification. This unpredictable variability profile of MWEs hinders modeling and processing them as {``}words-with-spaces{''} on the one hand, and as regular syntactic structures on the other hand. Since variability of MWEs is a matter of scale rather than a binary property, we propose a 2-dimensional language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues. We assess its relevance with respect to a linguistic benchmark and its utility for the tasks of VMWE classification and variant identification on a French corpus.
Cross-Lingual Learning-to-Rank with Shared Representations
Authors: Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, Kentaro Inui
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Learning-To-Rank, Machine Translation, Retrieval

Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.
Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation
Authors: Marianna Apidianaki, Guillaume Wisniewski, Anne Cocos, Chris Callison-Burch
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Translation

We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions. The original HyTER metric relied on hand-crafted paraphrase networks which restricted its applicability to new data. We test, for the first time, HyTER with automatically built paraphrase lattices. We show that although the metric obtains good results on small and carefully curated data with both manually and automatically selected substitutes, it achieves medium performance on much larger and noisier datasets, demonstrating the limits of the metric for tuning and evaluation of current MT systems.
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models
Authors: David Vilar
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Domain Adaptation, Machine Translation, speech-recognition, Speech Recognition, Translation

In this paper we explore the use of Learning Hidden Unit Contribution for the task of neural machine translation. The method was initially proposed in the context of speech recognition for adapting a general system to the specific acoustic characteristics of each speaker. Similar in spirit, in a machine translation framework we want to adapt a general system to a specific domain. We show that the proposed method achieves improvements of up to 2.6 BLEU points over a general system, and up to 6 BLEU points if the initial system has only been trained on out-of-domain data, a situation which may easily happen in practice. The good performance together with its short training time and small memory footprint make it a very attractive solution for domain adaptation.
Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation
Authors: Nima Pourdamghani, Marjan Ghazvininejad, Kevin Knight
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Morphological Analysis, Translation, Word Alignment

We present a method for improving word alignments using word similarities. This method is based on encouraging common alignment links between semantically similar words. We use word vectors trained on monolingual data to estimate similarity. Our experiments on translating fifteen languages into English show consistent BLEU score improvements across the languages.
The Computational Complexity of Distinctive Feature Minimization in Phonology
Authors: Hubie Chen, Mans Hulden
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

We analyze the complexity of the problem of determining whether a set of phonemes forms a natural class and, if so, that of finding the minimal feature specification for the class. A standard assumption in phonology is that finding a minimal feature specification is an automatic part of acquisition and generalization. We find that the natural class decision problem is tractable (i.e. is in P), while the minimization problem is not; the decision version of the problem which determines whether a natural class can be defined with $k$ features or less is NP-complete. We also show that, empirically, a greedy algorithm for finding minimal feature specifications will sometimes fail, and thus cannot be assumed to be the basis for human performance in solving the problem.
Leveraging Context Information for Natural Question Generation
Authors: Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Position, Question Generation, Question-Generation

The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer. It is useful for enlarging the training set of QA systems. Previous work has adopted sequence-to-sequence models that take a passage with an additional bit to indicate answer position as input. However, they do not explicitly model the information between answer and other context within the passage. We propose a model that matches the answer with the passage before generating the question. Experiments show that our model outperforms the existing state of the art using rich features.
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators
Authors: Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Text Classification, Transfer Learning

With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers. Recent studies on review helpfulness require plenty of labeled samples for each domain/category of interests. However, such an approach based on close-world assumption is not always practical, especially for domains with limited reviews or the {``}out-of-vocabulary{''} problem. Therefore, we propose a convolutional neural network (CNN) based model which leverages both word-level and character-based representations. To transfer knowledge between domains, we further extend our model to jointly model different domains with auxiliary domain discriminators. On the Amazon product review dataset, our approach significantly outperforms the state of the art in terms of both accuracy and cross-domain robustness.
Predicting Foreign Language Usage from English-Only Social Media Posts
Authors: Svitlana Volkova, Stephen Ranshous, Lawrence Phillips
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Cross-Lingual Transfer, Language Identification, Native Language Identification, Transfer Learning

Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English. We rely on this corpus to build predictive models to infer non-English languages that users speak exclusively from their English tweets. Unlike native language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-the-art machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of non-English languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transfer {--} the influence of non-English languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 languages in social media.
Effective Crowdsourcing for a New Type of Summarization Task
Authors: Youxuan Jiang, Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Walter Lasecki
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Vocal Bursts Type Prediction

Most summarization research focuses on summarizing the entire given text, but in practice readers are often interested in only one aspect of the document or conversation. We propose targeted summarization as an umbrella category for summarization tasks that intentionally consider only parts of the input data. This covers query-based summarization, update summarization, and a new task we propose where the goal is to summarize a particular aspect of a document. However, collecting data for this new task is hard because directly asking annotators (e.g., crowd workers) to write summaries leads to data with low accuracy when there are a large number of facts to include. We introduce a novel crowdsourcing workflow, Pin-Refine, that allows us to collect high-quality summaries for our task, a necessary step for the development of automatic systems.
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings
Authors: Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Chunking, Keyphrase Extraction, Named Entity Recognition (NER), Part-Of-Speech Tagging, Semantic Role Labeling, Word Embeddings

Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.
Objective Function Learning to Match Human Judgements for Optimization-Based Summarization
Authors: Maxime Peyrard, Iryna Gurevych
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments. In this work, we learn a summary-level scoring function $\theta$ including human judgments as supervision and automatically generated data as regularization. We extract summaries with a genetic algorithm using $\theta$ as a fitness function. We observe strong and promising performances across datasets in both automatic and manual evaluation.
Pruning Basic Elements for Better Automatic Evaluation of Summaries
Authors: Ukyo Honda, Tsutomu Hirao, Masaaki Nagata
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Word Embeddings, Word Similarity

We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE). Although the BE concept is widely used for the automated evaluation of summaries, its weakness is that it redundantly matches basic elements. To avoid this redundancy, pBE prunes basic elements by (1) disregarding frequency count of basic elements and (2) reducing semantically overlapped basic elements based on word similarity. Even though it is simple, pBE outperforms ROUGE in DUC datasets in most cases and achieves the highest rank correlation coefficient in TAC 2011 AESOP task.
Where Have I Heard This Story Before? Identifying Narrative Similarity in Movie Remakes
Authors: Snigdha Chaturvedi, Shashank Srivastava, Dan Roth
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Information Retrieval, Retrieval

People can identify correspondences between narratives in everyday life. For example, an analogy with the Cinderella story may be made in describing the unexpected success of an underdog in seemingly different stories. We present a new task and dataset for story understanding: identifying instances of similar narratives from a collection of narrative texts. We present an initial approach for this problem, which finds correspondences between narratives in terms of plot events, and resemblances between characters and their social relationships. Our approach yields an 8{\%} absolute improvement in performance over a competitive information-retrieval baseline on a novel dataset of plot summaries of 577 movie remakes from Wikipedia.
Non-Projective Dependency Parsing with Non-Local Transitions
Authors: Daniel Fern{\'a}ndez-Gonz{\'a}lez, Carlos G{\'o}mez-Rodr{\'\i}guez
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing

We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions. This avoids the need for long sequences of No-Arcs transitions to create long-distance arcs, thus alleviating error propagation. The resulting parser outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers.
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts
Authors: Annie Louis, Charles Sutton
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Topic Models

An essential aspect to understanding narratives is to grasp the interaction between characters in a story and the actions they take. We examine whether computational models can capture this interaction, when both character attributes and actions are expressed as complex natural language descriptions. We propose role-playing games as a testbed for this problem, and introduce a large corpus of game transcripts collected from online discussion forums. Using neural language models which combine character and action descriptions from these stories, we show that we can learn the latent ties. Action sequences are better predicted when the character performing the action is also taken into account, and vice versa for character attributes.
Gated Multi-Task Network for Text Classification
Authors: Liqiang Xiao, Honglun Zhang, Wenqing Chen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Classification, General Classification, Multi-Task Learning, text-classification, Text Classification

Multi-task learning with Convolutional Neural Network (CNN) has shown great success in many Natural Language Processing (NLP) tasks. This success can be largely attributed to the feature sharing by fusing some layers among tasks. However, most existing approaches just fully or proportionally share the features without distinguishing the helpfulness of them. By that the network would be confused by the helpless even harmful features, generating undesired interference between tasks. In this paper, we introduce gate mechanism into multi-task CNN and propose a new Gated Sharing Unit, which can filter the feature flows between tasks and greatly reduce the interference. Experiments on 9 text classification datasets shows that our approach can learn selection rules automatically and gain a great improvement over strong baselines.
Role-specific Language Models for Processing Recorded Neuropsychological Exams
Authors: Tuka Al Hanai, Rhoda Au, James Glass
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Epidemiology, Speaker Diarization, speech-recognition, Speech Recognition

Neuropsychological examinations are an important screening tool for the presence of cognitive conditions (e.g. Alzheimer{'}s, Parkinson{'}s Disease), and require a trained tester to conduct the exam through spoken interactions with the subject. While audio is relatively easy to record, it remains a challenge to automatically diarize (who spoke when?), decode (what did they say?), and assess a subject{'}s cognitive health. This paper demonstrates a method to determine the cognitive health (impaired or not) of 92 subjects, from audio that was diarized using an automatic speech recognition system trained on TED talks and on the structured language used by testers and subjects. Using leave-one-out cross validation and logistic regression modeling we show that even with noisily decoded data (81{\%} WER) we can still perform accurate enough diarization (0.02{\%} confusion rate) to determine the cognitive state of a subject (0.76 AUC).
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction
Authors: Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, Yun-Nung Chen
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: global-optimization, Intent Detection, Sentence, slot-filling, Slot Filling, Spoken Dialogue Systems, Spoken Language Understanding

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2{\%} and 1.9{\%} relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data
Authors: Sp Gella, ana, Frank Keller
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Question Answering, Visual Question Answering (VQA), Word Sense Disambiguation

Recent research in language and vision has developed models for predicting and disambiguating verbs from images. Here, we ask whether the predictions made by such models correspond to human intuitions about visual verbs. We show that the image regions a verb prediction model identifies as salient for a given verb correlate with the regions fixated by human observers performing a verb classification task.
The Emergence of Semantics in Neural Network Representations of Visual Information
Authors: Dhanush Dharmaretnam, Alona Fyshe
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Word vector models learn about semantics through corpora. Convolutional Neural Networks (CNNs) can learn about semantics through images. At the most abstract level, some of the information in these models must be shared, as they model the same real-world phenomena. Here we employ techniques previously used to detect semantic representations in the human brain to detect semantic representations in CNNs. We show the accumulation of semantic information in the layers of the CNN, and discover that, for misclassified images, the correct class can be recovered in intermediate layers of a CNN.
Visually Guided Spatial Relation Extraction from Text
Authors: Taher Rahgooy, Umar Manzoor, Parisa Kordjamshidi
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Activity Recognition, Image Captioning, Image Retrieval, Object Localization, Question Answering, Relation, Relation Extraction, Visual Question Answering (VQA)

Extraction of spatial relations from sentences with complex/nesting relationships is very challenging as often needs resolving inherent semantic ambiguities. We seek help from visual modality to fill the information gap in the text modality and resolve spatial semantic ambiguities. We use various recent vision and language datasets and techniques to train inter-modality alignment models, visual relationship classifiers and propose a novel global inference model to integrate these components into our structured output prediction model for spatial role and relation extraction. Our global inference model enables us to utilize the visual and geometric relationships between objects and improves the state-of-art results of spatial information extraction from text.
Scalable Wide and Deep Learning for Computer Assisted Coding
Authors: Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Deep Learning

In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.
Neural Network based Extreme Classification and Similarity Models for Product Matching
Authors: Kashif Shah, Selcuk Kopru, Jean-David Ruvini
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification

Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5{\%} in term of accuracy and are capable of extremely efficient training and inference.