pwc-archive/papers-with-abstracts huggingface dataset:
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models
Authors: Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Semantic Similarity, Semantic Textual Similarity

We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to corpus-based studies, a novel dataset demonstrates that {``}regular{''} shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators.
Determining Event Durations: Models and Error Analysis
Authors: Alakan Vempala, a, Eduardo Blanco, Alexis Palmer
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Question Answering

This paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
Authors: Yan Song, Shuming Shi, Jing Li, Haisong Zhang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Learning Word Embeddings, Part-Of-Speech Tagging, Word Embeddings, Word Similarity

In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction. In doing so, a direction vector is introduced for each word, whose embedding is thus learned by not only word co-occurrence patterns in its context, but also the directions of its contextual words. Theoretical and empirical studies on complexity illustrate that our model can be trained as efficient as the original skip-gram model, when compared to other extensions of the skip-gram model. Experimental results show that our model outperforms others on different datasets in semantic (word similarity measurement) and syntactic (part-of-speech tagging) evaluations, respectively.
Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model
Authors: Goran Glava{\v{s}}, Ivan Vuli{\'c}
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Inference, Paraphrase Generation, Text Simplification, Word Embeddings

We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.
Evaluating bilingual word embeddings on the long tail
Authors: Fabienne Braune, Viktor Hangya, Tobias Eder, Alex Fraser, er
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Bilingual Lexicon Induction, Machine Translation, Word Embeddings

Bilingual word embeddings are useful for bilingual lexicon induction, the task of mining translations of given words. Many studies have shown that bilingual word embeddings perform well for bilingual lexicon induction but they focused on frequent words in general domains. For many applications, bilingual lexicon induction of rare and domain-specific words is of critical importance. Therefore, we design a new task to evaluate bilingual word embeddings on rare words in different domains. We show that state-of-the-art approaches fail on this task and present simple new techniques to improve bilingual word embeddings for mining rare words. We release new gold standard datasets and code to stimulate research on this task.
Lexical Substitution for Evaluating Compositional Distributional Models
Authors: Maja Buljan, Sebastian Pad{\'o}, Jan {\v{S}}najder
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Inference, Sentence, Sentence Similarity, Sentiment Analysis

Compositional Distributional Semantic Models (CDSMs) model the meaning of phrases and sentences in vector space. They have been predominantly evaluated on limited, artificial tasks such as semantic sentence similarity on hand-constructed datasets. This paper argues for lexical substitution (LexSub) as a means to evaluate CDSMs. LexSub is a more natural task, enables us to evaluate meaning composition at the level of individual words, and provides a common ground to compare CDSMs with dedicated LexSub models. We create a LexSub dataset for CDSM evaluation from a corpus with manual {``}all-words{''} LexSub annotation. Our experiments indicate that the Practical Lexical Function CDSM outperforms simple component-wise CDSMs and performs on par with the context2vec LexSub model using the same context.
Olive Oil is Made \textitof Olives, Baby Oil is Made \textitfor Babies: Interpreting Noun Compounds Using Paraphrases in a Neural Model
Authors: Vered Shwartz, Chris Waterson
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Memorization, Relation, Relation Classification

Automatic interpretation of the relation between the constituents of a noun compound, e.g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications. Recent approaches are typically based on either noun-compound representations or paraphrases. While the former has initially shown promising results, recent work suggests that the success stems from memorizing single prototypical words for each relation. We explore a neural paraphrasing approach that demonstrates superior performance when such memorization is not possible.
Semantic Pleonasm Detection
Authors: Omid Kashefi, Andrew T. Lucas, Rebecca Hwa
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation

Pleonasms are words that are redundant. To aid the development of systems that detect pleonasms in text, we introduce an annotated corpus of semantic pleonasms. We validate the integrity of the corpus with interannotator agreement analyses. We also compare it against alternative resources in terms of their effects on several automatic redundancy detection methods.
Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks
Authors: Felipe Paula, Rodrigo Wilkens, Marco Idiart, Aline Villavicencio
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Similarity, Semantic Textual Similarity

Semantic Verbal Fluency tests have been used in the detection of certain clinical conditions, like Dementia. In particular, given a sequence of semantically related words, a large number of switches from one semantic class to another has been linked to clinical conditions. In this work, we investigate three similarity measures for automatically identifying switches in semantic chains: semantic similarity from a manually constructed resource, and word association strength and semantic relatedness, both calculated from corpora. This information is used for building classifiers to distinguish healthy controls from clinical cases with early stages of Alzheimer{'}s Disease and Mild Cognitive Deficits. The overall results indicate that for clinical conditions the classifiers that use these similarity measures outperform those that use a gold standard taxonomy.
Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees
Authors: Ola R{\o}nning, Daniel Hardt, Anders S{\o}gaard
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Feature Engineering, Multi-Task Learning

Sluice resolution in English is the problem of finding antecedents of \textit{wh}-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9{\%} error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26{\%} error reduction on embedded sluices in dialogue.
The Word Analogy Testing Caveat
Authors: Natalie Schluter
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Textual Similarity, Transfer Learning, Word Embeddings

There are some important problems in the evaluation of word embeddings using standard word analogy tests. In particular, in virtue of the assumptions made by systems generating the embeddings, these remain tests over randomness. We show that even supposing there were such word analogy regularities that should be detected in the word embeddings obtained via unsupervised means, standard word analogy test implementation practices provide distorted or contrived results. We raise concerns regarding the use of Principal Component Analysis to 2 or 3 dimensions as a provision of visual evidence for the existence of word analogy relations in embeddings. Finally, we propose some solutions to these problems.
Transition-Based Chinese AMR Parsing
Authors: Chuan Wang, Bin Li, Nianwen Xue
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: AMR Parsing, Reading Comprehension, Text Generation

This paper presents the first AMR parser built on the Chinese AMR bank. By applying a transition-based AMR parsing framework to Chinese, we first investigate how well the transitions first designed for English AMR parsing generalize to Chinese and provide a comparative analysis between the transitions for English and Chinese. We then perform a detailed error analysis to identify the major challenges in Chinese AMR parsing that we hope will inform future research in this area.
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Authors: Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification

Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment
Authors: Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya, Amit Sheth
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Multi-Task Learning, Sentiment Analysis

In recent past, social media has emerged as an active platform in the context of healthcare and medicine. In this paper, we present a study where medical user{'}s opinions on health-related issues are analyzed to capture the medical sentiment at a blog level. The medical sentiments can be studied in various facets such as medical condition, treatment, and medication that characterize the overall health status of the user. Considering these facets, we treat analysis of this information as a multi-task classification problem. In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog. Our evaluation shows promising results for our target tasks.
Looking for Structure in Lexical and Acoustic-Prosodic Entrainment Behaviors
Authors: Andreas Weise, Rivka Levitan
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6

Entrainment has been shown to occur for various linguistic features individually. Motivated by cognitive theories regarding linguistic entrainment, we analyze speakers{'} overall entrainment behaviors and search for an underlying structure. We consider various measures of both acoustic-prosodic and lexical entrainment, measuring the latter with a novel application of two previously introduced methods in addition to a standard high-frequency word measure. We present a negative result of our search, finding no meaningful correlations, clusters, or principal components in various entrainment measures, and discuss practical and theoretical implications.
A Comparison of Two Paraphrase Models for Taxonomy Augmentation
Authors: Vassilis Plachouras, Fabio Petroni, Timothy Nugent, Jochen L. Leidner
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Document Classification, Machine Translation, Paraphrase Generation, Translation, Vocal Bursts Valence Prediction

Taxonomies are often used to look up the concepts they contain in text documents (for instance, to classify a document). The more comprehensive the taxonomy, the higher recall the application has that uses the taxonomy. In this paper, we explore automatic taxonomy augmentation with paraphrases. We compare two state-of-the-art paraphrase models based on Moses, a statistical Machine Translation system, and a sequence-to-sequence neural network, trained on a paraphrase datasets with respect to their abilities to add novel nodes to an existing taxonomy from the risk domain. We conduct component-based and task-based evaluations. Our results show that paraphrasing is a viable method to enrich a taxonomy with more terms, and that Moses consistently outperforms the sequence-to-sequence neural model. To the best of our knowledge, this is the first approach to augment taxonomies with paraphrases.
A Laypeople Study on Terminology Identification across Domains and Task Definitions
Authors: Anna H{\"a}tty, Sabine Schulte im Walde
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Information Retrieval, Specificity

This paper introduces a new dataset of term annotation. Given that even experts vary significantly in their understanding of termhood, and that term identification is mostly performed as a binary task, we offer a novel perspective to explore the common, natural understanding of what constitutes a term: Laypeople annotate single-word and multi-word terms, across four domains and across four task definitions. Analyses based on inter-annotator agreement offer insights into differences in term specificity, term granularity and subtermhood.
Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding
Authors: Chun-Kai Wu, Richard Tzong-Han Tsai
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Information Retrieval

Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62{\%}. Tested 30 times, our system achieved an average improvement of 2.76{\%} over the current best system (26.86{\%} over baseline), a statistically significant result.
Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations
Authors: Prafulla Kumar Choubey, Kaushik Raju, Ruihong Huang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Text Segmentation, Text Summarization

Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation. We observed that the central event of a document usually has many coreferential event mentions that are scattered throughout the document for enabling a smooth transition of subtopics. Our empirical experiments, using gold event coreference relations, have shown that the central event of a document can be well identified by mining properties of event coreference chains. But the performance drops when switching to system predicted event coreference relations. In addition, we found that the central event can be more accurately identified by further considering the number of sub-events as well as the realis status of an event.
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding
Authors: Huasha Zhao, Yi Yang, Qiong Zhang, Luo Si
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Domain Adaptation, Machine Reading Comprehension, Multi-Task Learning, Named Entity Recognition (NER), Question Answering, Reading Comprehension, Transfer Learning

Entity recognition is a widely benchmarked task in natural language processing due to its massive applications. The state-of-the-art solution applies a neural architecture named BiLSTM-CRF to model the language sequences. In this paper, we propose an entity recognition system that improves this neural architecture with two novel techniques. The first technique is Multi-Task Data Selection, which ensures the consistency of data distribution and labeling guidelines between source and target datasets. The other one is constrained decoding using knowledge base. The decoder of the model operates at the document level, and leverages global and external information sources to further improve performance. Extensive experiments have been conducted to show the advantages of each technique. Our system achieves state-of-the-art results on the English entity recognition task in KBP 2017 official evaluation, and it also yields very strong results in other languages.
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift
Authors: Ajay Nagesh, Mihai Surdeanu
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Denoising, General Classification

We propose a novel approach to semi-supervised learning for information extraction that uses ladder networks (Rasmus et al., 2015). In particular, we focus on the task of named entity classification, defined as identifying the correct label (e.g., person or organization name) of an entity mention in a given context. Our approach is simple, efficient and has the benefit of being robust to semantic drift, a dominant problem in most semi-supervised learning systems. We empirically demonstrate the superior performance of our system compared to the state-of-the-art on two standard datasets for named entity classification. We obtain between 62{\%} and 200{\%} improvement over the state-of-art baseline on these two datasets.
Syntactically Aware Neural Architectures for Definition Extraction
Authors: Luis Espinosa-Anke, Steven Schockaert
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Benchmarking, Binary Classification, Classification, Definition Extraction, General Classification, Question Answering, Sentence, Sentence Classification, Word Sense Disambiguation

Automatically identifying definitional knowledge in text corpora (Definition Extraction or DE) is an important task with direct applications in, among others, Automatic Glossary Generation, Taxonomy Learning, Question Answering and Semantic Search. It is generally cast as a binary classification problem between definitional and non-definitional sentences. In this paper we present a set of neural architectures combining Convolutional and Recurrent Neural Networks, which are further enriched by incorporating linguistic information via syntactic dependencies. Our experimental results in the task of sentence classification, on two benchmarking DE datasets (one generic, one domain-specific), show that these models obtain consistent state of the art results. Furthermore, we demonstrate that models trained on clean Wikipedia-like definitions can successfully be applied to more noisy domain-specific corpora.
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing
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: ARC, Dependency Parsing

We propose an efficient dynamic oracle for training the 2-Planar transition-based parser, a linear-time parser with over 99{\%} coverage on non-projective syntactic corpora. This novel approach outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition, which can handle unrestricted non-projectivity.
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles
Authors: Guillaume Wisniewski, Oph{\'e}lie Lacroix, Fran{\c{c}}ois Yvon
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Sentence

This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees
Authors: Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing

Because the most common transition systems are projective, training a transition-based dependency parser often implies to either ignore or rewrite the non-projective training examples, which has an adverse impact on accuracy. In this work, we propose a simple modification of dynamic oracles, which enables the use of non-projective data when training projective parsers. Evaluation on 73 treebanks shows that our method achieves significant gains (+2 to +7 UAS for the most non-projective languages) and consistently outperforms traditional projectivization and pseudo-projectivization approaches.