pwc-archive/papers-with-abstracts huggingface dataset:
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
Authors: Jianfei Yu, Lu{\'\i}s Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, Emotion Classification, General Classification, Multi-Label Classification, Sentence, Sentiment Analysis, Sentiment Classification, Stock Market Prediction, Transfer Learning

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.
Syntactical Analysis of the Weaknesses of Sentiment Analyzers
Authors: Rohil Verma, Samuel Kim, David Walter
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Sentiment Analysis

We carry out a syntactic analysis of two state-of-the-art sentiment analyzers, Google Cloud Natural Language and Stanford CoreNLP, to assess their classification accuracy on sentences with negative polarity items. We were motivated by the absence of studies investigating sentiment analyzer performance on sentences with polarity items, a common construct in human language. Our analysis focuses on two sentential structures: downward entailment and non-monotone quantifiers; and demonstrates weaknesses of Google Natural Language and CoreNLP in capturing polarity item information. We describe the particular syntactic phenomenon that these analyzers fail to understand that any ideal sentiment analyzer must. We also provide a set of 150 test sentences that any ideal sentiment analyzer must be able to understand.
Is Nike female? Exploring the role of sound symbolism in predicting brand name gender
Authors: Sridhar Moorthy, Ruth Pogacar, Samin Khan, Yang Xu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

Are brand names such as Nike female or male? Previous research suggests that the sound of a person{'}s first name is associated with the person{'}s gender, but no research has tried to use this knowledge to assess the gender of brand names. We present a simple computational approach that uses sound symbolism to address this open issue. Consistent with previous research, a model trained on various linguistic features of name endings predicts human gender with high accuracy. Applying this model to a data set of over a thousand commercially-traded brands in 17 product categories, our results reveal an overall bias toward male names, cutting across both male-oriented product categories as well as female-oriented categories. In addition, we find variation within categories, suggesting that firms might be seeking to imbue their brands with differentiating characteristics as part of their competitive strategy.
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Authors: Nayeon Lee, Chien-Sheng Wu, Pascale Fung
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Fact Checking, Question Answering, Retrieval, Stance Detection

Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80{\%}, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
Authors: Ryuji Kano, Yasuhide Miura, Motoki Taniguchi, Yan-Ying Chen, Francine Chen, Tomoko Ohkuma
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Extractive Summarization, Informativeness

We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.
Identifying Locus of Control in Social Media Language
Authors: Masoud Rouhizadeh, Kokil Jaidka, Laura Smith, H. Andrew Schwartz, Anneke Buffone, Lyle Ungar
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances. Although control is a core concept underlying rhetorical style, it is not clear whether control is expressed by how or by what authors write. We explore the roles of syntax and semantics in expressing users{'} sense of control {--}i.e. being {``}controlled by{''} or {``}in control of{''} their circumstances{--} in a corpus of annotated Facebook posts. We present rich insights into these linguistic aspects and find that while the language signaling control is easy to identify, it is more challenging to label it is internally or externally controlled, with lexical features outperforming syntactic features at the task. Our findings could have important implications for studying self-expression in social media.
Somm: Into the Model
Authors: Shengli Hu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: BIG-bench Machine Learning, Information Retrieval, model, Open-Domain Question Answering, Question Answering, Reading Comprehension

To what extent could the sommelier profession, or wine stewardship, be displaced by machine leaning algorithms? There are at least three essential skills that make a qualified sommelier: wine theory, blind tasting, and beverage service, as exemplified in the rigorous certification processes of certified sommeliers and above (advanced and master) with the most authoritative body in the industry, the Court of Master Sommelier (hereafter CMS). We propose and train corresponding machine learning models that match these skills, and compare algorithmic results with real data collected from a large group of wine professionals. We find that our machine learning models outperform human sommeliers on most tasks {---} most notably in the section of blind tasting, where hierarchically supervised Latent Dirichlet Allocation outperforms sommeliers{'} judgment calls by over 6{\%} in terms of F1-score; and in the section of beverage service, especially wine and food pairing, a modified Siamese neural network based on BiLSTM achieves better results than sommeliers by 2{\%}. This demonstrates, contrary to popular opinion in the industry, that the sommelier profession is at least to some extent automatable, barring economic (Kleinberg et al., 2017) and psychological (Dietvorst et al., 2015) complications.
Fine-Grained Emotion Detection in Health-Related Online Posts
Authors: Hamed Khanpour, Cornelia Caragea
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Emotion Recognition

Detecting fine-grained emotions in online health communities provides insightful information about patients{'} emotional states. However, current computational approaches to emotion detection from health-related posts focus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features. In this paper, we take a step further and propose to detect fine-grained emotion types from health-related posts and show how high-level and abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Authors: Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Event Detection, Sentence

Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.
Valency-Augmented Dependency Parsing
Authors: Tianze Shi, Lillian Lee
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing

We present a complete, automated, and efficient approach for utilizing valency analysis in making dependency parsing decisions. It includes extraction of valency patterns, a probabilistic model for tagging these patterns, and a joint decoding process that explicitly considers the number and types of each token{'}s syntactic dependents. On 53 treebanks representing 41 languages in the Universal Dependencies data, we find that incorporating valency information yields higher precision and F1 scores on the core arguments (subjects and complements) and functional relations (e.g., auxiliaries) that we employ for valency analysis. Precision on core arguments improves from 80.87 to 85.43. We further show that our approach can be applied to an ostensibly different formalism and dataset, Tree Adjoining Grammar as extracted from the Penn Treebank; there, we outperform the previous state-of-the-art labeled attachment score by 0.7. Finally, we explore the potential of extending valency patterns beyond their traditional domain by confirming their helpfulness in improving PP attachment decisions.
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing
Authors: Daniel Fern{\'a}ndez-Gonz{\'a}lez, Carlos G{\'o}mez-Rodr{\'\i}guez
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

We introduce novel dynamic oracles for training two of the most accurate known shift-reduce algorithms for constituent parsing: the top-down and in-order transition-based parsers. In both cases, the dynamic oracles manage to notably increase their accuracy, in comparison to that obtained by performing classic static training. In addition, by improving the performance of the state-of-the-art in-order shift-reduce parser, we achieve the best accuracy to date (92.0 F1) obtained by a fully-supervised single-model greedy shift-reduce constituent parser on the WSJ benchmark.
Synthetic Data Made to Order: The Case of Parsing
Authors: Dingquan Wang, Jason Eisner
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Cross-Lingual Transfer, Dependency Parsing, Language Modeling, Language Modelling, Word Embeddings

To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such {``}made to order{''} artificial languages.
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention
Authors: Fuli Luo, Tianyu Liu, Zexue He, Qiaolin Xia, Zhifang Sui, Baobao Chang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Word Sense Disambiguation

The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context. Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge such as the gloss which defines the meaning of a word sense. Recent studies have shown that incorporating glosses into neural networks for WSD has made significant improvement. However, the previous models usually build the context representation and gloss representation separately. In this paper, we find that the learning for the context and gloss representation can benefit from each other. Gloss can help to highlight the important words in the context, thus building a better context representation. Context can also help to locate the key words in the gloss of the correct word sense. Therefore, we introduce a co-attention mechanism to generate co-dependent representations for the context and gloss. Furthermore, in order to capture both word-level and sentence-level information, we extend the attention mechanism in a hierarchical fashion. Experimental results show that our model achieves the state-of-the-art results on several standard English all-words WSD test datasets.
Streaming word similarity mining on the cheap
Authors: Olof G{\"o}rnerup, Daniel Gillblad
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Document Classification, Word Alignment, Word Embeddings, Word Similarity

Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explicitly counting second-order co-occurrences. The method rests on the observation that words that are highly correlated with respect to such counts are also highly similar with respect to first-order co-occurrences. Using buffers of co-occurred words per word to count second-order co-occurrences, we can then estimate similarities in a single pass over data without having to do prohibitively expensive similarity calculations. We demonstrate that this approach is scalable, converges rapidly, behaves robustly under parameter changes, and that it captures word similarities on par with those given by state-of-the-art word embeddings.
Memory, Show the Way: Memory Based Few Shot Word Representation Learning
Authors: Jingyuan Sun, Shaonan Wang, Cheng-qing Zong
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, NER, Representation Learning, Sentiment Analysis, Sentiment Classification, Word Similarity

Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation. This is in stark contrast with human who can guess the meaning of a word from one or a few referents only. In this paper, we propose Mem2Vec, a memory based embedding learning method capable of acquiring high quality word representations from fairly limited context. Our method directly adapts the representations produced by a DSM with a longterm memory to guide its guess of a novel word. Based on a pre-trained embedding space, the proposed method delivers impressive performance on two challenging few-shot word similarity tasks. Embeddings learned with our method also lead to considerable improvements over strong baselines on NER and sentiment classification.
Disambiguated skip-gram model
Authors: Karol Grzegorczyk, Marcin Kurdziel
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Image Captioning, model, Word Embeddings, Word Sense Disambiguation, Word Sense Induction

We present disambiguated skip-gram: a neural-probabilistic model for learning multi-sense distributed representations of words. Disambiguated skip-gram jointly estimates a skip-gram-like context word prediction model and a word sense disambiguation model. Unlike previous probabilistic models for learning multi-sense word embeddings, disambiguated skip-gram is end-to-end differentiable and can be interpreted as a simple feed-forward neural network. We also introduce an effective pruning strategy for the embeddings learned by disambiguated skip-gram. This allows us to control the granularity of representations learned by our model. In experimental evaluation disambiguated skip-gram improves state-of-the are results in several word sense induction benchmarks.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images
Authors: Melissa Ailem, Bo-Wen Zhang, Aurelien Bellet, Pascal Denis, Fei Sha
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Coreference Resolution, Image Classification, Question Answering, Retrieval, Sentiment Analysis, Word Embeddings, Word Similarity

Several recent studies have shown the benefits of combining language and perception to infer word embeddings. These multimodal approaches either simply combine pre-trained textual and visual representations (e.g. features extracted from convolutional neural networks), or use the latter to bias the learning of textual word embeddings. In this work, we propose a novel probabilistic model to formalize how linguistic and perceptual inputs can work in concert to explain the observed word-context pairs in a text corpus. Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data. Extensive experimental studies validate the proposed model. Concretely, on the tasks of assessing pairwise word similarity and image/caption retrieval, our approach attains equally competitive or stronger results when compared to other state-of-the-art multimodal models.
Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation
Authors: Murhaf Fares, Stephan Oepen, Erik Velldal
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Information Retrieval, Multi-Task Learning, Question Answering, Transfer Learning, Word Embeddings

In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun{--}noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.
Preposition Sense Disambiguation and Representation
Authors: Hongyu Gong, Jiaqi Mu, Suma Bhat, Pramod Viswanath
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

Prepositions are highly polysemous, and their variegated senses encode significant semantic information. In this paper we match each preposition{'}s left- and right context, and their interplay to the geometry of the word vectors to the left and right of the preposition. Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense disambiguation (PSD) algorithm, which is comparable to and better than state-of-the-art on two benchmark datasets. Our reliance on no linguistic tool allows us to scale the PSD algorithm to a large corpus and learn sense-specific preposition representations. The crucial abstraction of preposition senses as word representations permits their use in downstream applications{--}phrasal verb paraphrasing and preposition selection{--}with new state-of-the-art results.
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
Authors: Tom Bosc, Pascal Vincent
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Document Classification, Machine Translation, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. It exploits the inherent recursivity of dictionaries by encouraging consistency between the representations it uses as inputs and the representations it produces as outputs. The resulting embeddings are shown to capture semantic similarity better than regular distributional methods and other dictionary-based methods. In addition, our method shows strong performance when trained exclusively on dictionary data and generalizes in one shot.
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources
Authors: Gabriel Stanovsky, Mark Hopkins
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Natural Language Inference, Question Answering, Word Embeddings

We propose Odd-Man-Out, a novel task which aims to test different properties of word representations. An Odd-Man-Out puzzle is composed of 5 (or more) words, and requires the system to choose the one which does not belong with the others. We show that this simple setup is capable of teasing out various properties of different popular lexical resources (like WordNet and pre-trained word embeddings), while being intuitive enough to annotate on a large scale. In addition, we propose a novel technique for training multi-prototype word representations, based on unsupervised clustering of ELMo embeddings, and show that it surpasses all other representations on all Odd-Man-Out collections.
Neural Multitask Learning for Simile Recognition
Authors: Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, Guoping Hu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Language Modeling, Language Modelling, Sentence, Sentence Classification

Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.