pwc-archive/papers-with-abstracts huggingface dataset:
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations
Authors: Nikos Athanasiou, Elias Iosif, Alex Potamianos, ros
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dimensionality Reduction, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Neural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations. Successful mappings let us expand to the full lexicon of concrete nouns using the assumption that similarity of meaning implies similar neural activation patterns. In this paper, we propose a computational model that estimates semantic similarity in the neural activation space and investigates the relative performance of this model for various natural language processing tasks. Despite the simplicity of the proposed model and the very small number of example words used to bootstrap it, the neural activation semantic model performs surprisingly well compared to state-of-the-art word embeddings. Specifically, the neural activation semantic model performs better than the state-of-the-art for the task of semantic similarity estimation between very similar or very dissimilar words, while performing well on other tasks such as entailment and word categorization. These are strong indications that neural activation semantic models can not only shed some light into human cognition but also contribute to computation models for certain tasks.
Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning
Authors: Shabnam Tafreshi, Mona Diab
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Emotion Detection and Classification, General Classification, Multi-Task Learning, Sentence

Detection and classification of emotion categories expressed by a sentence is a challenging task due to subjectivity of emotion. To date, most of the models are trained and evaluated on single genre and when used to predict emotion in different genre their performance drops by a large margin. To address the issue of robustness, we model the problem within a joint multi-task learning framework. We train this model with a multigenre emotion corpus to predict emotions across various genre. Each genre is represented as a separate task, we use soft parameter shared layers across the various tasks. our experimental results show that this model improves the results across the various genres, compared to a single genre training in the same neural net architecture.
How emotional are you? Neural Architectures for Emotion Intensity Prediction in Microblogs
Authors: Devang Kulshreshtha, Pranav Goel, Anil Kumar Singh
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Multi-Task Learning, Transfer Learning

Social media based micro-blogging sites like Twitter have become a common source of real-time information (impacting organizations and their strategies, and are used for expressing emotions and opinions. Automated analysis of such content therefore rises in importance. To this end, we explore the viability of using deep neural networks on the specific task of emotion intensity prediction in tweets. We propose a neural architecture combining convolutional and fully connected layers in a non-sequential manner - done for the first time in context of natural language based tasks. Combined with lexicon-based features along with transfer learning, our model achieves state-of-the-art performance, outperforming the previous system by 0.044 or 4.4{\%} Pearson correlation on the WASSA{'}17 EmoInt shared task dataset. We investigate the performance of deep multi-task learning models trained for all emotions at once in a unified architecture and get encouraging results. Experiments performed on evaluating correlation between emotion pairs offer interesting insights into the relationship between them.
Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media
Authors: Isabel Cachola, Eric Holgate, Daniel Preo{\c{t}}iuc-Pietro, Junyi Jessy Li
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis

Vulgarity is a common linguistic expression and is used to perform several linguistic functions. Understanding their usage can aid both linguistic and psychological phenomena as well as benefit downstream natural language processing applications such as sentiment analysis. This study performs a large-scale, data-driven empirical analysis of vulgar words using social media data. We analyze the socio-cultural and pragmatic aspects of vulgarity using tweets from users with known demographics. Further, we collect sentiment ratings for vulgar tweets to study the relationship between the use of vulgar words and perceived sentiment and show that explicitly modeling vulgar words can boost sentiment analysis performance.
Clausal Modifiers in the Grammar Matrix
Authors: Kristen Howell, Olga Zamaraeva
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We extend the coverage of an existing grammar customization system to clausal modifiers, also referred to as adverbial clauses. We present an analysis, taking a typologically-driven approach to account for this phenomenon across the world{'}s languages, which we implement in the Grammar Matrix customization system (Bender et al., 2002, 2010). Testing our analysis on testsuites from five genetically and geographically diverse languages that were not considered in development, we achieve 88.4{\%} coverage and 1.5{\%} overgeneration.
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP Models
Authors: Henry Moss, David Leslie, Paul Rayson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Document Classification, General Classification, Model Selection, Part-Of-Speech Tagging, Sentiment Analysis, Sentiment Classification

K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires random partitioning of the data and so our performance estimates are in fact stochastic, with variability that can be substantial for natural language processing tasks. We demonstrate that these unstable estimates cannot be relied upon for effective parameter tuning. The resulting tuned parameters are highly sensitive to how our data is partitioned, meaning that we often select sub-optimal parameter choices and have serious reproducibility issues. Instead, we propose to use the less variable J-K-fold CV, in which J independent K-fold cross validations are used to assess performance. Our main contributions are extending J-K-fold CV from performance estimation to parameter tuning and investigating how to choose J and K. We argue that variability is more important than bias for effective tuning and so advocate lower choices of K than are typically seen in the NLP literature and instead use the saved computation to increase J. To demonstrate the generality of our recommendations we investigate a wide range of case-studies: sentiment classification (both general and target-specific), part-of-speech tagging and document classification.
Incremental Natural Language Processing: Challenges, Strategies, and Evaluation
Authors: Arne K{\"o}hn
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialogue Understanding

Incrementality is ubiquitous in human-human interaction and beneficial for human-computer interaction. It has been a topic of research in different parts of the NLP community, mostly with focus on the specific topic at hand even though incremental systems have to deal with similar challenges regardless of domain. In this survey, I consolidate and categorize the approaches, identifying similarities and differences in the computation and data, and show trade-offs that have to be considered. A focus lies on evaluating incremental systems because the standard metrics often fail to capture the incremental properties of a system and coming up with a suitable evaluation scheme is non-trivial.
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions
Authors: Lori Moon, Christos Christodoulopoulos, Cynthia Fisher, S. Franco, ra, Dan Roth
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Acquisition, Part-Of-Speech Tagging, Semantic Role Labeling, Sentence

This paper describes the augmentation of an existing corpus of child-directed speech. The resulting corpus is a gold-standard labeled corpus for supervised learning of semantic role labels in adult-child dialogues. Semantic role labeling (SRL) models assign semantic roles to sentence constituents, thus indicating who has done what to whom (and in what way). The current corpus is derived from the Adam files in the Brown corpus (Brown 1973) of the CHILDES corpora, and augments the partial annotation described in Connor et al. (2010). It provides labels for both semantic arguments of verbs and semantic arguments of prepositions. The semantic role labels and senses of verbs follow Propbank guidelines Kingsbury and Palmer, 2002; Gildea and Palmer 2002; Palmer et al., 2005) and those for prepositions follow Srikumar and Roth (2011). The corpus was annotated by two annotators. Inter-annotator agreement is given separately for prepositions and verbs, and for adult speech and child speech. Overall, across child and adult samples, including verbs and prepositions, the kappa score for sense is 72.6, for the number of semantic-role-bearing arguments, the kappa score is 77.4, for identical semantic role labels on a given argument, the kappa score is 91.1, for the span of semantic role labels, and the kappa for agreement is 93.9. The sense and number of arguments was often open to multiple interpretations in child speech, due to the rapidly changing discourse and omission of constituents in production. Annotators used a discourse context window of ten sentences before and ten sentences after the target utterance to determine the annotation labels. The derived corpus is available for use in CHAT (MacWhinney, 2000) and XML format.
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Authors: Chenggang Mi, Yating Yang, Lei Wang, Xi Zhou, Tonghai Jiang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Cross-Lingual Word Embeddings, Language Modeling, Language Modelling, Machine Translation, Translation, Word Embeddings

To enrich vocabulary of low resource settings, we proposed a novel method which identify loanwords in monolingual corpora. More specifically, we first use cross-lingual word embeddings as the core feature to generate semantically related candidates based on comparable corpora and a small bilingual lexicon; then, a log-linear model which combines several shallow features such as pronunciation similarity and hybrid language model features to predict the final results. In this paper, we use Uyghur as the receipt language and try to detect loanwords in four donor languages: Arabic, Chinese, Persian and Russian. We conduct two groups of experiments to evaluate the effectiveness of our proposed approach: loanword identification and OOV translation in four language pairs and eight translation directions (Uyghur-Arabic, Arabic-Uyghur, Uyghur-Chinese, Chinese-Uyghur, Uyghur-Persian, Persian-Uyghur, Uyghur-Russian, and Russian-Uyghur). Experimental results on loanword identification show that our method outperforms other baseline models significantly. Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0.5-0.9 BLEU improvements)
Adaptive Weighting for Neural Machine Translation
Authors: Yachao Li, Junhui Li, Min Zhang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, NMT, Translation

In the popular sequence to sequence (seq2seq) neural machine translation (NMT), there exist many weighted sum models (WSMs), each of which takes a set of input and generates one output. However, the weights in a WSM are independent of each other and fixed for all inputs, suggesting that by ignoring different needs of inputs, the WSM lacks effective control on the influence of each input. In this paper, we propose adaptive weighting for WSMs to control the contribution of each input. Specifically, we apply adaptive weighting for both GRU and the output state in NMT. Experimentation on Chinese-to-English translation and English-to-German translation demonstrates that the proposed adaptive weighting is able to much improve translation accuracy by achieving significant improvement of 1.49 and 0.92 BLEU points for the two translation tasks. Moreover, we discuss in-depth on what type of information is encoded in the encoder and how information influences the generation of target words in the decoder.
Generic refinement of expressive grammar formalisms with an application to discontinuous constituent parsing
Authors: Kilian Gebhardt
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We formulate a generalization of Petrov et al. (2006){'}s split/merge algorithm for interpreted regular tree grammars (Koller and Kuhlmann, 2011), which capture a large class of grammar formalisms. We evaluate its effectiveness empirically on the task of discontinuous constituent parsing with two mildly context-sensitive grammar formalisms: linear context-free rewriting systems (Vijay-Shanker et al., 1987) as well as hybrid grammars (Nederhof and Vogler, 2014).
An Empirical Investigation of Error Types in Vietnamese Parsing
Authors: Quy Nguyen, Yusuke Miyao, Hiroshi Noji, Nhung Nguyen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Constituency Parsing, Part-Of-Speech Tagging, POS, POS Tagging, Vietnamese Parsing

Syntactic parsing plays a crucial role in improving the quality of natural language processing tasks. Although there have been several research projects on syntactic parsing in Vietnamese, the parsing quality has been far inferior than those reported in major languages, such as English and Chinese. In this work, we evaluated representative constituency parsing models on a Vietnamese Treebank to look for the most suitable parsing method for Vietnamese. We then combined the advantages of automatic and manual analysis to investigate errors produced by the experimented parsers and find the reasons for them. Our analysis focused on three possible sources of parsing errors, namely limited training data, part-of-speech (POS) tagging errors, and ambiguous constructions. As a result, we found that the last two sources, which frequently appear in Vietnamese text, significantly attributed to the poor performance of Vietnamese parsing.
Learning with Noise-Contrastive Estimation: Easing training by learning to scale
Authors: Matthieu Labeau, Alex Allauzen, re
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech Recognition

Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it avoids the computational bottleneck caused by the output softmax. In this paper, we analyse and explain some of the weaknesses of this objective function, linked to the mechanism of self-normalization, by closely monitoring comparative experiments. We then explore several remedies and modifications to propose tractable and efficient NCE training strategies. In particular, we propose to make the scaling factor a trainable parameter of the model, and to use the noise distribution to initialize the output bias. These solutions, yet simple, yield stable and competitive performances in either small and large scale language modelling tasks.
Parallel Corpora for bi-lingual English-Ethiopian Languages Statistical Machine Translation
Authors: Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Binyam Ephrem, Tewodros Abebe, Wondimagegnhue Tsegaye, Amanuel Lemma, Tsegaye Andargie, Seifedin Shifaw
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge{'}ez. The corpora are used for conducting a bi-directional statistical machine translation experiments. The BLEU scores of the bi-directional Statistical Machine Translation (SMT) systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT specially when the targets are Ethiopian languages. Now we are working towards an optimal alignment for a bi-directional English-Ethiopian languages SMT.
Combining Information-Weighted Sequence Alignment and Sound Correspondence Models for Improved Cognate Detection
Authors: Johannes Dellert
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Clustering

Methods for automated cognate detection in historical linguistics invariably build on some measure of form similarity which is designed to capture the remaining systematic similarities between cognate word forms after thousands of years of divergence. A wide range of clustering and classification algorithms has been explored for the purpose, whereas possible improvements on the level of pairwise form similarity measures have not been the main focus of research. The approach presented in this paper improves on this core component of cognate detection systems by a novel combination of information weighting, a technique for putting less weight on reoccurring morphological material, with sound correspondence modeling by means of pointwise mutual information. In evaluations on expert cognacy judgments over a subset of the IPA-encoded NorthEuraLex database, the combination of both techniques is shown to lead to considerable improvements in average precision for binary cognate detection, and modest improvements for distance-based cognate clustering.
Tailoring Neural Architectures for Translating from Morphologically Rich Languages
Authors: Peyman Passban, Andy Way, Qun Liu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, NMT, Sentence, Translation

A morphologically complex word (MCW) is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. When translating from morphologically rich languages (MRLs), a source word could be mapped to several words or even a full sentence on the target side, which means an MCW should not be treated as an atomic unit. In order to provide better translations for MRLs, we boost the existing neural machine translation (NMT) architecture with a double- channel encoder and a double-attentive decoder. The main goal targeted in this research is to provide richer information on the encoder side and redesign the decoder accordingly to benefit from such information. Our experimental results demonstrate that we could achieve our goal as the proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English.
deepQuest: A Framework for Neural-based Quality Estimation
Authors: Julia Ive, Fr{\'e}d{\'e}ric Blain, Lucia Specia
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, Machine Translation, Sentence, Translation

Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.
Butterfly Effects in Frame Semantic Parsing: impact of data processing on model ranking
Authors: Alex Kabbach, re, Corentin Ribeyre, Aur{\'e}lie Herbelot
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Semantic Parsing

Knowing the state-of-the-art for a particular task is an essential component of any computational linguistics investigation. But can we be truly confident that the current state-of-the-art is indeed the best performing model? In this paper, we study the case of frame semantic parsing, a well-established task with multiple shared datasets. We show that in spite of all the care taken to provide a standard evaluation resource, small variations in data processing can have dramatic consequences for ranking parser performance. This leads us to propose an open-source standardized processing pipeline, which can be shared and reused for robust model comparison.
Sensitivity to Input Order: Evaluation of an Incremental and Memory-Limited Bayesian Cross-Situational Word Learning Model
Authors: Sepideh Sadeghi, Matthias Scheutz
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sensitivity

We present a variation of the incremental and memory-limited algorithm in (Sadeghi et al., 2017) for Bayesian cross-situational word learning and evaluate the model in terms of its functional performance and its sensitivity to input order. We show that the functional performance of our sub-optimal model on corpus data is close to that of its optimal counterpart (Frank et al., 2009), while only the sub-optimal model is capable of predicting the input order effects reported in experimental studies.
Sentence Weighting for Neural Machine Translation Domain Adaptation
Authors: Shiqi Zhang, Deyi Xiong
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

In this paper, we propose a new sentence weighting method for the domain adaptation of neural machine translation. We introduce a domain similarity metric to evaluate the relevance between a sentence and an available entire domain dataset. The similarity of each sentence to the target domain is calculated with various methods. The computed similarity is then integrated into the training objective to weight sentences. The adaptation results on both IWSLT Chinese-English TED task and a task with only synthetic training parallel data show that our sentence weighting method is able to achieve an significant improvement over strong baselines.
Quantifying training challenges of dependency parsers
Authors: Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Cross-Lingual Transfer, Dependency Parsing

Not all dependencies are equal when training a dependency parser: some are straightforward enough to be learned with only a sample of data, others embed more complexity. This work introduces a series of metrics to quantify those differences, and thereby to expose the shortcomings of various parsing algorithms and strategies. Apart from a more thorough comparison of parsing systems, these new tools also prove useful for characterizing the information conveyed by cross-lingual parsers, in a quantitative but still interpretable way.