pwc-archive/papers-with-abstracts huggingface dataset:
Producing Unseen Morphological Variants in Statistical Machine Translation
Authors: Matthias Huck, Ale{\v{s}} Tamchyna, Ond{\v{r}}ej Bojar, Alex Fraser, er
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Translation

Translating into morphologically rich languages is difficult. Although the coverage of lemmas may be reasonable, many morphological variants cannot be learned from the training data. We present a statistical translation system that is able to produce these inflected word forms. Different from most previous work, we do not separate morphological prediction from lexical choice into two consecutive steps. Our approach is novel in that it is integrated in decoding and takes advantage of context information from both the source language and the target language sides.
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources
Authors: Mohammad Taher Pilehvar, Nigel Collier
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

We put forward an approach that exploits the knowledge encoded in lexical resources in order to induce representations for words that were not encountered frequently during training. Our approach provides an advantage over the past work in that it enables vocabulary expansion not only for morphological variations, but also for infrequent domain specific terms. We performed evaluations in different settings, showing that the technique can provide consistent improvements on multiple benchmarks across domains.
Large-scale evaluation of dependency-based DSMs: Are they worth the effort?
Authors: Gabriella Lapesa, Stefan Evert
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Semantic Similarity, Semantic Textual Similarity

This paper presents a large-scale evaluation study of dependency-based distributional semantic models. We evaluate dependency-filtered and dependency-structured DSMs in a number of standard semantic similarity tasks, systematically exploring their parameter space in order to give them a {``}fair shot{''} against window-based models. Our results show that properly tuned window-based DSMs still outperform the dependency-based models in most tasks. There appears to be little need for the language-dependent resources and computational cost associated with syntactic analysis.
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis
Authors: Ivan Sanchez, Sebastian Riedel
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Natural Language Inference, Word Embeddings

One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity
Cross-Lingual Syntactically Informed Distributed Word Representations
Authors: Ivan Vuli{\'c}
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Bilingual Lexicon Induction, Entity Linking, Information Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Transfer Learning, Translation, Word Embeddings, Word Similarity, Word Translation

We develop a novel cross-lingual word representation model which injects syntactic information through dependency-based contexts into a shared cross-lingual word vector space. The model, termed CL-DepEmb, is based on the following assumptions: (1) dependency relations are largely language-independent, at least for related languages and prominent dependency links such as direct objects, as evidenced by the Universal Dependencies project; (2) word translation equivalents take similar grammatical roles in a sentence and are therefore substitutable within their syntactic contexts. Experiments with several language pairs on word similarity and bilingual lexicon induction, two fundamental semantic tasks emphasising semantic similarity, suggest the usefulness of the proposed syntactically informed cross-lingual word vector spaces. Improvements are observed in both tasks over standard cross-lingual {``}offline mapping{''} baselines trained using the same setup and an equal level of bilingual supervision.
Using Word Embedding for Cross-Language Plagiarism Detection
Authors: J{\'e}r{\'e}my Ferrero, Laurent Besacier, Didier Schwab, Fr{\'e}d{\'e}ric Agn{\`e}s
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Machine Translation, Sentence, Word Embeddings

This paper proposes to use distributed representation of words (word embeddings) in cross-language textual similarity detection. The main contributions of this paper are the following: (a) we introduce new cross-language similarity detection methods based on distributed representation of words; (b) we combine the different methods proposed to verify their complementarity and finally obtain an overall F1 score of 89.15{\%} for English-French similarity detection at chunk level (88.5{\%} at sentence level) on a very challenging corpus.
Pulling Out the Stops: Rethinking Stopword Removal for Topic Models
Authors: Alex Schofield, ra, M{\aa}ns Magnusson, David Mimno
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Language Modeling, Language Modelling, Topic Models

It is often assumed that topic models benefit from the use of a manually curated stopword list. Constructing this list is time-consuming and often subject to user judgments about what kinds of words are important to the model and the application. Although stopword removal clearly affects which word types appear as most probable terms in topics, we argue that this improvement is superficial, and that topic inference benefits little from the practice of removing stopwords beyond very frequent terms. Removing corpus-specific stopwords after model inference is more transparent and produces similar results to removing those words prior to inference.
Measuring Topic Coherence through Optimal Word Buckets
Authors: Nitin Ramrakhiyani, Sachin Pawar, Swapnil Hingmire, Girish Palshikar
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Information Retrieval, Retrieval, text-classification, Text Classification, Topic Models, Word Embeddings

Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification. To measure quality of Latent Dirichlet Allocation (LDA) based topics learned from text, we propose a novel approach based on grouping of topic words into buckets (TBuckets). A single large bucket signifies a single coherent theme, in turn indicating high topic coherence. TBuckets uses word embeddings of topic words and employs singular value decomposition (SVD) and Integer Linear Programming based optimization to create coherent word buckets. TBuckets outperforms the state-of-the-art techniques when evaluated using 3 publicly available datasets and on another one proposed in this paper.
A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification
Authors: Shiou Tian Hsu, Changsung Moon, Paul Jones, Nagiza Samatova
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Sentence, Sentence Classification, Sentence Embeddings

The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases. Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. In this paper, we propose a structure-independent {`}Gated Representation Alignment{'} (GRA) model that blends a phrase-focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6{\%}) of fine-grained sentiment ratings, when compared to other structure-independent baselines. We also show comparable results to several structure-dependent methods. Finally, we analyzed the effect of our alignment mechanism and found that this is critical to the effectiveness of the CNN-RNN hybrid.
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization
Authors: Giannis Nikolentzos, Polykarpos Meladianos, Fran{\c{c}}ois Rousseau, Yannis Stavrakas, Michalis Vazirgiannis
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Text Categorization, Word Embeddings

Recently, there has been a lot of activity in learning distributed representations of words in vector spaces. Although there are models capable of learning high-quality distributed representations of words, how to generate vector representations of the same quality for phrases or documents still remains a challenge. In this paper, we propose to model each document as a multivariate Gaussian distribution based on the distributed representations of its words. We then measure the similarity between two documents based on the similarity of their distributions. Experiments on eight standard text categorization datasets demonstrate the effectiveness of the proposed approach in comparison with state-of-the-art methods.
Derivation of Document Vectors from Adaptation of LSTM Language Model
Authors: Wei Li, Brian Mak
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Genre classification, Language Modeling, Language Modelling

In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequency-inverse document frequency (TF-IDF) vector. One major shortcoming of the frequency-based TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document. This paper proposes a novel distributed vector representation of a document, which will be labeled as DV-LSTM, and is derived from the result of adapting a long short-term memory recurrent neural network language model by the document. DV-LSTM is expected to capture some high-level sequential information in the document, which other current document representations fail to do. It was evaluated in document genre classification in the Brown Corpus and the BNC Baby Corpus. The results show that DV-LSTM significantly outperforms TF-IDF vector and paragraph vector (PV-DM) in most cases, and their combinations may further improve the classification performance.
Real-Time Keyword Extraction from Conversations
Authors: Polykarpos Meladianos, Antoine Tixier, Ioannis Nikolentzos, Michalis Vazirgiannis
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automatic Speech Recognition (ASR), Keyword Extraction, Speech Recognition, Word Embeddings

We introduce a novel method to extract keywords from meeting speech in real-time. Our approach builds on the graph-of-words representation of text and leverages the k-core decomposition algorithm and properties of submodular functions. We outperform multiple baselines in a real-time scenario emulated from the AMI and ICSI meeting corpora. Evaluation is conducted against both extractive and abstractive gold standard using two standard performance metrics and a newer one based on word embeddings.
Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
Authors: Simon Keizer, Markus Guhe, Heriberto Cuay{\'a}huitl, Ioannis Efstathiou, Klaus-Peter Engelbrecht, Mihai Dobre, Alex Lascarides, Oliver Lemon
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Deep Reinforcement Learning, Persuasion Strategies, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}. The comparison is based on human subjects playing games against artificial game-playing agents ({`}bots{'}) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.
Unsupervised Dialogue Act Induction using Gaussian Mixtures
Authors: Tom{\'a}{\v{s}} Brychc{\'\i}n, Pavel Kr{\'a}l
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Topic Models

This paper introduces a new unsupervised approach for dialogue act induction. Given the sequence of dialogue utterances, the task is to assign them the labels representing their function in the dialogue. Utterances are represented as real-valued vectors encoding their meaning. We model the dialogue as Hidden Markov model with emission probabilities estimated by Gaussian mixtures. We use Gibbs sampling for posterior inference. We present the results on the standard Switchboard-DAMSL corpus. Our algorithm achieves promising results compared with strong supervised baselines and outperforms other unsupervised algorithms.
Grounding Language by Continuous Observation of Instruction Following
Authors: Ting Han, David Schlangen
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Instruction Following

Grounded semantics is typically learnt from utterance-level meaning representations (e.g., successful database retrievals, denoted objects in images, moves in a game). We explore learning word and utterance meanings by continuous observation of the actions of an instruction follower (IF). While an instruction giver (IG) provided a verbal description of a configuration of objects, IF recreated it using a GUI. Aligning these GUI actions to sub-utterance chunks allows a simple maximum entropy model to associate them as chunk meaning better than just providing it with the utterance-final configuration. This shows that semantics useful for incremental (word-by-word) application, as required in natural dialogue, might also be better acquired from incremental settings.
Mapping the Perfect via Translation Mining
Authors: Martijn van der Klis, Bert Le Bruyn, Henri{\"e}tte de Swart
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Translation

Semantic analyses of the Perfect often defeat their own purpose: by restricting their attention to {`}real{'} perfects (like the English one), they implicitly assume the Perfect has predefined meanings and usages. We turn the tables and focus on form, using data extracted from multilingual parallel corpora to automatically generate semantic maps (Haspelmath, 1997) of the sequence {`}Have/Be + past participle{'} in five European languages (German, English, Spanish, French, Dutch). This technique, which we dub Translation Mining, has been applied before in the lexical domain (W{\"a}lchli and Cysouw, 2012) but we showcase its application at the level of the grammar.
Efficient, Compositional, Order-sensitive n-gram Embeddings
Authors: Adam Poliak, Pushpendre Rastogi, M. Patrick Martin, Benjamin Van Durme
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

We propose ECO: a new way to generate embeddings for phrases that is Efficient, Compositional, and Order-sensitive. Our method creates decompositional embeddings for words offline and combines them to create new embeddings for phrases in real time. Unlike other approaches, ECO can create embeddings for phrases not seen during training. We evaluate ECO on supervised and unsupervised tasks and demonstrate that creating phrase embeddings that are sensitive to word order can help downstream tasks.
Integrating Semantic Knowledge into Lexical Embeddings Based on Information Content Measurement
Authors: Hsin-Yang Wang, Wei-Yun Ma
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Document Classification, Information Retrieval, Question Answering, Word Embeddings, Word Similarity

Distributional word representations are widely used in NLP tasks. These representations are based on an assumption that words with a similar context tend to have a similar meaning. To improve the quality of the context-based embeddings, many researches have explored how to make full use of existing lexical resources. In this paper, we argue that while we incorporate the prior knowledge with context-based embeddings, words with different occurrences should be treated differently. Therefore, we propose to rely on the measurement of information content to control the degree of applying prior knowledge into context-based embeddings - different words would have different learning rates when adjusting their embeddings. In the result, we demonstrate that our embeddings get significant improvements on two different tasks: Word Similarity and Analogical Reasoning.
Improving Neural Knowledge Base Completion with Cross-Lingual Projections
Authors: Patrick Klein, Simone Paolo Ponzetto, Goran Glava{\v{s}}
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Knowledge Base Completion, Link Prediction, Reading Comprehension, Word Embeddings

In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.
Modelling metaphor with attribute-based semantics
Authors: Luana Bulat, Stephen Clark, Ekaterina Shutova
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Attribute, Machine Translation, Natural Language Inference, Word Embeddings

One of the key problems in computational metaphor modelling is finding the optimal level of abstraction of semantic representations, such that these are able to capture and generalise metaphorical mechanisms. In this paper we present the first metaphor identification method that uses representations constructed from property norms. Such norms have been previously shown to provide a cognitively plausible representation of concepts in terms of semantic properties. Our results demonstrate that such property-based semantic representations provide a suitable model of cross-domain knowledge projection in metaphors, outperforming standard distributional models on a metaphor identification task.
When a Red Herring in Not a Red Herring: Using Compositional Methods to Detect Non-Compositional Phrases
Authors: Julie Weeds, Thomas Kober, Jeremy Reffin, David Weir
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

Non-compositional phrases such as \textit{red herring} and weakly compositional phrases such as \textit{spelling bee} are an integral part of natural language (Sag, 2002). They are also the phrases that are difficult, or even impossible, for good compositional distributional models of semantics. Compositionality detection therefore provides a good testbed for compositional methods. We compare an integrated compositional distributional approach, using sparse high dimensional representations, with the ad-hoc compositional approach of applying simple composition operations to state-of-the-art neural embeddings.
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language
Authors: Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Semantic Textual Similarity, Word Embeddings, Word Sense Disambiguation

Up to date, the majority of computational models still determines the semantic relatedness between words (or larger linguistic units) on the type level. In this paper, we compare and extend multi-sense embeddings, in order to model and utilise word senses on the token level. We focus on the challenging class of complex verbs, and evaluate the model variants on various semantic tasks: semantic classification; predicting compositionality; and detecting non-literal language usage. While there is no overall best model, all models significantly outperform a word2vec single-sense skip baseline, thus demonstrating the need to distinguish between word senses in a distributional semantic model.
A Dataset for Multi-Target Stance Detection
Authors: Parinaz Sobhani, Diana Inkpen, Xiaodan Zhu
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, General Classification, Position, Stance Classification, Stance Detection

Current models for stance classification often treat each target independently, but in many applications, there exist natural dependencies among targets, e.g., stance towards two or more politicians in an election or towards several brands of the same product. In this paper, we focus on the problem of multi-target stance detection. We present a new dataset that we built for this task. Furthermore, We experiment with several neural models on the dataset and show that they are more effective in jointly modeling the overall position towards two related targets compared to independent predictions and other models of joint learning, such as cascading classification. We make the new dataset publicly available, in order to facilitate further research in multi-target stance classification.
Single and Cross-domain Polarity Classification using String Kernels
Authors: Rosa M. Gim{\'e}nez-P{\'e}rez, Marc Franco-Salvador, Paolo Rosso
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, Domain Adaptation, General Classification, Text Classification

The polarity classification task aims at automatically identifying whether a subjective text is positive or negative. When the target domain is different from those where a model was trained, we refer to a cross-domain setting. That setting usually implies the use of a domain adaptation method. In this work, we study the single and cross-domain polarity classification tasks from the string kernels perspective. Contrary to classical domain adaptation methods, which employ texts from both domains to detect pivot features, we do not use the target domain for training. Our approach detects the lexical peculiarities that characterise the text polarity and maps them into a domain independent space by means of kernel discriminant analysis. Experimental results show state-of-the-art performance in single and cross-domain polarity classification.
Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering
Authors: Jo{\~a}o Sedoc, Daniel Preo{\c{t}}iuc-Pietro, Lyle Ungar
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Clustering, Position, Sentiment Analysis, Word Similarity

Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains. We develop a method that automatically extends word-level ratings to unrated words using signed clustering of vector space word representations along with affect ratings. We use our method to determine a word{'}s valence and arousal, which determine its position on the circumplex model of affect, the most popular dimensional model of emotion. Our method achieves superior out-of-sample word rating prediction on both affective dimensions across three different languages when compared to state-of-the-art word similarity based methods. Our method can assist building word ratings for new languages and improve downstream tasks such as sentiment analysis and emotion detection.