pwc-archive/papers-with-abstracts huggingface dataset:
A Rich Morphological Tagger for English: Exploring the Cross-Linguistic Tradeoff Between Morphology and Syntax
Authors: Christo Kirov, John Sylak-Glassman, Rebecca Knowles, Ryan Cotterell, Matt Post
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, Machine Translation, Morphological Analysis, Morphological Tagging, Translation

A traditional claim in linguistics is that all human languages are equally expressive{---}able to convey the same wide range of meanings. Morphologically rich languages, such as Czech, rely on overt inflectional and derivational morphology to convey many semantic distinctions. Languages with comparatively limited morphology, such as English, should be able to accomplish the same using a combination of syntactic and contextual cues. We capitalize on this idea by training a tagger for English that uses syntactic features obtained by automatic parsing to recover complex morphological tags projected from Czech. The high accuracy of the resulting model provides quantitative confirmation of the underlying linguistic hypothesis of equal expressivity, and bodes well for future improvements in downstream HLT tasks including machine translation.
Comparing Character-level Neural Language Models Using a Lexical Decision Task
Authors: Ga{\"e}l Le Godais, Tal Linzen, Emmanuel Dupoux
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

What is the information captured by neural network models of language? We address this question in the case of character-level recurrent neural language models. These models do not have explicit word representations; do they acquire implicit ones? We assess the lexical capacity of a network using the lexical decision task common in psycholinguistics: the system is required to decide whether or not a string of characters forms a word. We explore how accuracy on this task is affected by the architecture of the network, focusing on cell type (LSTM vs. SRN), depth and width. We also compare these architectural properties to a simple count of the parameters of the network. The overall number of parameters in the network turns out to be the most important predictor of accuracy; in particular, there is little evidence that deeper networks are beneficial for this task.
Optimal encoding! - Information Theory constrains article omission in newspaper headlines
Authors: Robin Lemke, Eva Horch, Ingo Reich
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Articles

In this paper we pursue the hypothesis that the distribution of article omission specifically is constrained by principles of Information Theory (Shannon 1948). In particular, Information Theory predicts a stronger preference for article omission before nouns which are relatively unpredictable in context of the preceding words. We investigated article omission in German newspaper headlines with a corpus and acceptability rating study. Both support our hypothesis: Articles are inserted more often before unpredictable nouns and subjects perceive article omission before predictable nouns as more well-formed than before unpredictable ones. This suggests that information theoretic principles constrain the distribution of article omission in headlines.
A Computational Analysis of the Language of Drug Addiction
Authors: Carlo Strapparava, Rada Mihalcea
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

We present a computational analysis of the language of drug users when talking about their drug experiences. We introduce a new dataset of over 4,000 descriptions of experiences reported by users of four main drug types, and show that we can predict with an F1-score of up to 88{\%} the drug behind a certain experience. We also perform an analysis of the dominant psycholinguistic processes and dominant emotions associated with each drug type, which sheds light on the characteristics of drug users.
A Practical Perspective on Latent Structured Prediction for Coreference Resolution
Authors: Iryna Haponchyk, Aless Moschitti, ro
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: coreference-resolution, Coreference Resolution, feature selection, Structured Prediction, Text Categorization

Latent structured prediction theory proposes powerful methods such as Latent Structural SVM (LSSVM), which can potentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the latent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learning with LSSVM. We analyze the intricacies that may have made initial attempts to use LSSVM fail, i.e., a huge training time and much lower accuracy produced by Kruskal{'}s spanning tree algorithm. In this respect, we also propose a new effective feature selection approach for improving system efficiency. The results show that LSP, if correctly parameterized, produces the same performance as LSSVM, being much more efficient.
On the Need of Cross Validation for Discourse Relation Classification
Authors: Wei Shi, Vera Demberg
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, General Classification, Implicit Discourse Relation Classification, Language Modeling, Language Modelling, Machine Translation, Question Answering, Relation, Relation Classification

The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21 of the PDTB and test on section 23, which includes a total of 761 implicit discourse relations. In this paper, we{'}d like to make a methodological point, arguing that the standard test set is too small to draw conclusions about whether the inclusion of certain features constitute a genuine improvement, or whether one got lucky with some properties of the test set, and argue for the adoption of cross validation for the discourse relation classification task by the community.
Effective search space reduction for spell correction using character neural embeddings
Authors: P, Harshit e
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Optical Character Recognition (OCR)

We present a novel, unsupervised, and distance measure agnostic method for search space reduction in spell correction using neural character embeddings. The embeddings are learned by skip-gram word2vec training on sequences generated from dictionary words in a phonetic information-retentive manner. We report a very high performance in terms of both success rates and reduction of search space on the Birkbeck spelling error corpus. To the best of our knowledge, this is the first application of word2vec to spell correction.
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis
Authors: Ryan Cotterell, Adam Poliak, Benjamin Van Durme, Jason Eisner
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

The popular skip-gram model induces word embeddings by exploiting the signal from word-context coocurrence. We offer a new interpretation of skip-gram based on exponential family PCA-a form of matrix factorization to generalize the skip-gram model to tensor factorization. In turn, this lets us train embeddings through richer higher-order coocurrences, e.g., triples that include positional information (to incorporate syntax) or morphological information (to share parameters across related words). We experiment on 40 languages and show our model improves upon skip-gram.
On-line Dialogue Policy Learning with Companion Teaching
Authors: Lu Chen, Runzhe Yang, Cheng Chang, Zihao Ye, Xiang Zhou, Kai Yu
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dialogue Management

On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios. Poor initial policy can easily lead to bad user experience and consequently fail to attract sufficient users for policy training. A novel framework, companion teaching, is proposed to include a human teacher in the dialogue policy training loop to address the cold start problem. Here, dialogue policy is trained using not only user{'}s reward, but also teacher{'}s example action as well as estimated immediate reward at turn level. Simulation experiments showed that, with small number of human teaching dialogues, the proposed approach can effectively improve user experience at the beginning and smoothly lead to good performance with more user interaction data.
Morphological Analysis without Expert Annotation
Authors: Garrett Nicolai, Grzegorz Kondrak
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: LEMMA, Morphological Analysis, Morphological Tagging, TAG

The task of morphological analysis is to produce a complete list of lemma+tag analyses for a given word-form. We propose a discriminative string transduction approach which exploits plain inflection tables and raw text corpora, thus obviating the need for expert annotation. Experiments on four languages demonstrate that our system has much higher coverage than a hand-engineered FST analyzer, and is more accurate than a state-of-the-art morphological tagger.
Morphological Analysis of the Dravidian Language Family
Authors: Arun Kumar, Ryan Cotterell, Llu{\'\i}s Padr{\'o}, Antoni Oliver
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Morphological Analysis, Segmentation

The Dravidian languages are one of the most widely spoken language families in the world, yet there are very few annotated resources available to NLP researchers. To remedy this, we create DravMorph, a corpus annotated for morphological segmentation and part-of-speech. Additionally, we exploit novel features and higher-order models to set state-of-the-art results on these corpora on both tasks, beating techniques proposed in the literature by as much as 4 points in segmentation F1.
BabelDomains: Large-Scale Domain Labeling of Lexical Resources
Authors: Jose Camacho-Collados, Roberto Navigli
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Clustering, Domain Adaptation, Hypernym Discovery, Sentiment Analysis, Text Categorization, Word Sense Disambiguation

In this paper we present BabelDomains, a unified resource which provides lexical items with information about domains of knowledge. We propose an automatic method that uses knowledge from various lexical resources, exploiting both distributional and graph-based clues, to accurately propagate domain information. We evaluate our methodology intrinsically on two lexical resources (WordNet and BabelNet), achieving a precision over 80{\%} in both cases. Finally, we show the potential of BabelDomains in a supervised learning setting, clustering training data by domain for hypernym discovery.
A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages
Authors: Nizar Habash, Nasser Zalmout, Dima Taji, Hieu Hoang, Maverick Alzate
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Benchmarking, Machine Translation, Translation

We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRC-Acquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.
Cross-lingual tagger evaluation without test data
Authors: {\v{Z}}eljko Agi{\'c}, Barbara Plank, Anders S{\o}gaard
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: POS

We address the challenge of cross-lingual POS tagger evaluation in absence of manually annotated test data. We put forth and evaluate two dictionary-based metrics. On the tasks of accuracy prediction and system ranking, we reveal that these metrics are reliable enough to approximate test set-based evaluation, and at the same time lean enough to support assessment for truly low-resource languages.
Legal NERC with ontologies, Wikipedia and curriculum learning
Authors: Cristian Cardellino, Milagro Teruel, Laura Alonso Alemany, Serena Villata
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, Semantic Role Labeling

In this paper, we present a Wikipedia-based approach to develop resources for the legal domain. We establish a mapping between a legal domain ontology, LKIF (Hoekstra et al. 2007), and a Wikipedia-based ontology, YAGO (Suchanek et al. 2007), and through that we populate LKIF. Moreover, we use the mentions of those entities in Wikipedia text to train a specific Named Entity Recognizer and Classifier. We find that this classifier works well in the Wikipedia, but, as could be expected, performance decreases in a corpus of judgments of the European Court of Human Rights. However, this tool will be used as a preprocess for human annotation. We resort to a technique called {``}curriculum learning{''} aimed to overcome problems of overfitting by learning increasingly more complex concepts. However, we find that in this particular setting, the method works best by learning from most specific to most general concepts, not the other way round.
The Content Types Dataset: a New Resource to Explore Semantic and Functional Characteristics of Texts
Authors: Rachele Sprugnoli, Tommaso Caselli, Sara Tonelli, Giovanni Moretti
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, General Classification, Sentiment Analysis

This paper presents a new resource, called Content Types Dataset, to promote the analysis of texts as a composition of units with specific semantic and functional roles. By developing this dataset, we also introduce a new NLP task for the automatic classification of Content Types. The annotation scheme and the dataset are described together with two sets of classification experiments.
Continuous N-gram Representations for Authorship Attribution
Authors: Yunita Sari, Andreas Vlachos, Mark Stevenson
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Authorship Attribution, General Classification, Text Classification

This paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.
Reconstructing the house from the ad: Structured prediction on real estate classifieds
Authors: Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, Named Entity Recognition (NER), Structured Prediction

In this paper, we address the (to the best of our knowledge) new problem of extracting a structured description of real estate properties from their natural language descriptions in classifieds. We survey and present several models to (a) identify important entities of a property (e.g.,rooms) from classifieds and (b) structure them into a tree format, with the entities as nodes and edges representing a part-of relation. Experiments show that a graph-based system deriving the tree from an initially fully connected entity graph, outperforms a transition-based system starting from only the entity nodes, since it better reconstructs the tree.
Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario
Authors: M. Amin Farajian, Marco Turchi, Matteo Negri, Nicola Bertoldi, Marcello Federico
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

State-of-the-art neural machine translation (NMT) systems are generally trained on specific domains by carefully selecting the training sets and applying proper domain adaptation techniques. In this paper we consider the real world scenario in which the target domain is not predefined, hence the system should be able to translate text from multiple domains. We compare the performance of a generic NMT system and phrase-based statistical machine translation (PBMT) system by training them on a generic parallel corpus composed of data from different domains. Our results on multi-domain English-French data show that, in these realistic conditions, PBMT outperforms its neural counterpart. This raises the question: is NMT ready for deployment as a generic/multi-purpose MT backbone in real-world settings?
Improving ROUGE for Timeline Summarization
Authors: Sebastian Martschat, Katja Markert
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Semantic Similarity, Semantic Textual Similarity, Timeline Summarization

Current evaluation metrics for timeline summarization either ignore the temporal aspect of the task or require strict date matching. We introduce variants of ROUGE that allow alignment of daily summaries via temporal distance or semantic similarity. We argue for the suitability of these variants in a theoretical analysis and demonstrate it in a battery of task-specific tests.
To Sing like a Mockingbird
Authors: Lorenzo Gatti, G{\"o}zde {\"O}zbal, Oliviero Stock, Carlo Strapparava
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

Musical parody, i.e. the act of changing the lyrics of an existing and very well-known song, is a commonly used technique for creating catchy advertising tunes and for mocking people or events. Here we describe a system for automatically producing a musical parody, starting from a corpus of songs. The system can automatically identify characterizing words and concepts related to a novel text, which are taken from the daily news. These concepts are then used as seeds to appropriately replace part of the original lyrics of a song, using metrical, rhyming and lexical constraints. Finally, the parody can be sung with a singing speech synthesizer, with no intervention from the user.
K-best Iterative Viterbi Parsing
Authors: Katsuhiko Hayashi, Masaaki Nagata
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4

This paper presents an efficient and optimal parsing algorithm for probabilistic context-free grammars (PCFGs). To achieve faster parsing, our proposal employs a pruning technique to reduce unnecessary edges in the search space. The key is to conduct repetitively Viterbi inside and outside parsing, while gradually expanding the search space to efficiently compute heuristic bounds used for pruning. Our experimental results using the English Penn Treebank corpus show that the proposed algorithm is faster than the standard CKY parsing algorithm. In addition, we also show how to extend this algorithm to extract k-best Viterbi parse trees.
PP Attachment: Where do We Stand?
Authors: Dani{\"e}l de Kok, Jianqiang Ma, Corina Dima, Erhard Hinrichs
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Word Embeddings

Prepostitional phrase (PP) attachment is a well known challenge to parsing. In this paper, we combine the insights of different works, namely: (1) treating PP attachment as a classification task with an arbitrary number of attachment candidates; (2) using auxiliary distributions to augment the data beyond the hand-annotated training set; (3) using topological fields to get information about the distribution of PP attachment throughout clauses and (4) using state-of-the-art techniques such as word embeddings and neural networks. We show that jointly using these techniques leads to substantial improvements. We also conduct a qualitative analysis to gauge where the ceiling of the task is in a realistic setup.
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers
Authors: Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Active Learning, Dependency Parsing

This paper formalizes a sound extension of dynamic oracles to global training, in the frame of transition-based dependency parsers. By dispensing with the pre-computation of references, this extension widens the training strategies that can be entertained for such parsers; we show this by revisiting two standard training procedures, early-update and max-violation, to correct some of their search space sampling biases. Experimentally, on the SPMRL treebanks, this improvement increases the similarity between the train and test distributions and yields performance improvements up to 0.7 UAS, without any computation overhead.
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks
Authors: Maximin Coavoux, Beno{\^\i}t Crabb{\'e}
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Constituency Parsing, Morphological Analysis, Morphological Tagging, POS

We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important interfaces of constituency parsing with auxiliary tasks supervised at the word level: (i) part-of-speech (POS) and morphological tagging, (ii) functional label prediction. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring either predicted POS or morphological tags, and outputs labelled dependency trees.
Neural Automatic Post-Editing Using Prior Alignment and Reranking
Authors: Santanu Pal, Sudip Kumar Naskar, Mihaela Vela, Qun Liu, Josef van Genabith
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Automatic Post-Editing, Machine Translation, NMT, Reranking, Re-Ranking, Translation

We present a second-stage machine translation (MT) system based on a neural machine translation (NMT) approach to automatic post-editing (APE) that improves the translation quality provided by a first-stage MT system. Our APE system (APE{\_}Sym) is an extended version of an attention based NMT model with bilingual symmetry employing bidirectional models, mt{--}pe and pe{--}mt. APE translations produced by our system show statistically significant improvements over the first-stage MT, phrase-based APE and the best reported score on the WMT 2016 APE dataset by a previous neural APE system. Re-ranking (APE{\_}Rerank) of the n-best translations from the phrase-based APE and APE{\_}Sym systems provides further substantial improvements over the symmetric neural APE model. Human evaluation confirms that the APE{\_}Rerank generated PE translations improve on the previous best neural APE system at WMT 2016.
Improving Evaluation of Document-level Machine Translation Quality Estimation
Authors: Yvette Graham, Qingsong Ma, Timothy Baldwin, Qun Liu, Carla Parra, Carolina Scarton
Date: 2017-04-01 00:00:00
Proceeding: EACL 2017 4
Tasks: Document Level Machine Translation, Machine Translation, Translation, valid

Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable. In this paper, we explore the validity of human annotations currently employed in the evaluation of document-level quality estimation for machine translation (MT). We demonstrate the degree to which MT system rankings are dependent on weights employed in the construction of the gold standard, before proposing direct human assessment as a valid alternative. Experiments show direct assessment (DA) scores for documents to be highly reliable, achieving a correlation of above 0.9 in a self-replication experiment, in addition to a substantial estimated cost reduction through quality controlled crowd-sourcing. The original gold standard based on post-edits incurs a 10{--}20 times greater cost than DA.