pwc-archive/papers-with-abstracts huggingface dataset:
The IMAGACT4ALL Ontology of Animated Images: Implications for Theoretical and Machine Translation of Action Verbs from English-Indian Languages
Authors: Pitambar Behera, Sharmin Muzaffar, Atul Ku. Ojha, Girish Jha
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

Action verbs are one of the frequently occurring linguistic elements in any given natural language as the speakers use them during every linguistic intercourse. However, each language expresses action verbs in its own inherently unique manner by categorization. One verb can refer to several interpretations of actions and one action can be expressed by more than one verb. The inter-language and intra-language variations create ambiguity for the translation of languages from the source language to target language with respect to action verbs. IMAGACT is a corpus-based ontological platform of action verbs translated from prototypic animated images explained in English and Italian as meta-languages. In this paper, we are presenting the issues and challenges in translating action verbs of Indian languages as target and English as source language by observing the animated images. Among the ten Indian languages which have been annotated so far on the platform are Sanskrit, Hindi, Urdu, Odia (Oriya), Bengali, Manipuri, Tamil, Assamese, Magahi and Marathi. Out of them, Manipuri belongs to the Sino-Tibetan, Tamil comes off the Dravidian and the rest owe their genesis to the Indo-Aryan language family. One of the issues is that the one-word morphological English verbs are translated into most of the Indian languages as verbs having more than one-word form; for instance as in the case of conjunct, compound, serial verbs and so on. We are further presenting a cross-lingual comparison of action verbs among Indian languages. In addition, we are also dealing with the issues in disambiguating animated images by the L1 native speakers using competence-based judgements and the theoretical and machine translation implications they bear.
Crowdsourcing-based Annotation of Emotions in Filipino and English Tweets
Authors: Fermin Roberto Lapitan, Riza Theresa Batista-Navarro, Eliezer Albacea
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Emotion Recognition

The automatic analysis of emotions conveyed in social media content, e.g., tweets, has many beneficial applications. In the Philippines, one of the most disaster-prone countries in the world, such methods could potentially enable first responders to make timely decisions despite the risk of data deluge. However, recognising emotions expressed in Philippine-generated tweets, which are mostly written in Filipino, English or a mix of both, is a non-trivial task. In order to facilitate the development of natural language processing (NLP) methods that will automate such type of analysis, we have built a corpus of tweets whose predominant emotions have been manually annotated by means of crowdsourcing. Defining measures ensuring that only high-quality annotations were retained, we have produced a gold standard corpus of 1,146 emotion-labelled Filipino and English tweets. We validate the value of this manually produced resource by demonstrating that an automatic emotion-prediction method based on the use of a publicly available word-emotion association lexicon was unable to reproduce the labels assigned via crowdsourcing. While we are planning to make a few extensions to the corpus in the near future, its current version has been made publicly available in order to foster the development of emotion analysis methods based on advanced Filipino and English NLP.
Sentiment Analysis of Tweets in Three Indian Languages
Authors: Shanta Phani, Shibamouli Lahiri, Arindam Biswas
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification, Opinion Mining, Sentiment Analysis

In this paper, we describe the results of sentiment analysis on tweets in three Indian languages {--} Bengali, Hindi, and Tamil. We used the recently released SAIL dataset (Patra et al., 2015), and obtained state-of-the-art results in all three languages. Our features are simple, robust, scalable, and language-independent. Further, we show that these simple features provide better results than more complex and language-specific features, in two separate classification tasks. Detailed feature analysis and error analysis have been reported, along with learning curves for Hindi and Bengali.
Dealing with Linguistic Divergences in English-Bhojpuri Machine Translation
Authors: Pitambar Behera, Neha Mourya, P, V ey, ana
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

In Machine Translation, divergence is one of the major barriers which plays a deciding role in determining the efficiency of the system at hand. Translation divergences originate when there is structural discrepancies between the input and the output languages. It can be of various types based on the issues we are addressing to such as linguistic, cultural, communicative and so on. Owing to the fact that two languages owe their origin to different language families, linguistic divergences emerge. The present study attempts at categorizing different types of linguistic divergences: the lexical-semantic and syntactic. In addition, it also helps identify and resolve the divergent linguistic features between English as source language and Bhojpuri as target language pair. Dorr{'}s theoretical framework (1994, 1994a) has been followed in the classification and resolution procedure. Furthermore, so far as the methodology is concerned, we have adhered to the Dorr{'}s Lexical Conceptual Structure for the resolution of divergences. This research will prove to be beneficial for developing efficient MT systems if the mentioned factors are incorporated considering the inherent structural constraints between source and target languages.ated considering the inherent structural constraints between SL and TL pairs.
The development of a web corpus of Hindi language and corpus-based comparative studies to Japanese
Authors: Miki Nishioka, Shiro Akasegawa
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

In this paper, we discuss our creation of a web corpus of spoken Hindi (COSH), one of the Indo-Aryan languages spoken mainly in the Indian subcontinent. We also point out notable problems we{'}ve encountered in the web corpus and the special concordancer. After observing the kind of technical problems we encountered, especially regarding annotation tagged by Shiva Reddy{'}s tagger, we argue how they can be solved when using COSH for linguistic studies. Finally, we mention the kinds of linguistic research that we non-native speakers of Hindi can do using the corpus, especially in pragmatics and semantics, and from a comparative viewpoint to Japanese.
Automatic Creation of a Sentence Aligned Sinhala-Tamil Parallel Corpus
Authors: Riyafa Abdul Hameed, Nadeeshani Pathirennehelage, Anusha Ihalapathirana, Maryam Ziyad Mohamed, Surangika Ranathunga, Sanath Jayasena, Gihan Dias, Fern, S o, areka
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Sentence, Translation, Word Alignment

A sentence aligned parallel corpus is an important prerequisite in statistical machine translation. However, manual creation of such a parallel corpus is time consuming, and requires experts fluent in both languages. Automatic creation of a sentence aligned parallel corpus using parallel text is the solution to this problem. In this paper, we present the first ever empirical evaluation carried out to identify the best method to automatically create a sentence aligned Sinhala-Tamil parallel corpus. Annual reports from Sri Lankan government institutions were used as the parallel text for aligning. Despite both Sinhala and Tamil being under-resourced languages, we were able to achieve an F-score value of 0.791 using a hybrid approach that makes use of a bilingual dictionary.
Clustering-based Phonetic Projection in Mismatched Crowdsourcing Channels for Low-resourced ASR
Authors: Wenda Chen, Mark Hasegawa-Johnson, Nancy Chen, Preethi Jyothi, Lav Varshney
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Clustering

Acquiring labeled speech for low-resource languages is a difficult task in the absence of native speakers of the language. One solution to this problem involves collecting speech transcriptions from crowd workers who are foreign or non-native speakers of a given target language. From these mismatched transcriptions, one can derive probabilistic phone transcriptions that are defined over the set of all target language phones using a noisy channel model. This paper extends prior work on deriving probabilistic transcriptions (PTs) from mismatched transcriptions by 1) modelling multilingual channels and 2) introducing a clustering-based phonetic mapping technique to improve the quality of PTs. Mismatched crowdsourcing for multilingual channels has certain properties of projection mapping, e.g., it can be interpreted as a clustering based on singular value decomposition of the segment alignments. To this end, we explore the use of distinctive feature weights, lexical tone confusions, and a two-step clustering algorithm to learn projections of phoneme segments from mismatched multilingual transcriber languages to the target language. We evaluate our techniques using mismatched transcriptions for Cantonese speech acquired from native English and Mandarin speakers. We observe a 5-9{\%} relative reduction in phone error rate for the predicted Cantonese phone transcriptions using our proposed techniques compared with the previous PT method.
Improving the Morphological Analysis of Classical Sanskrit
Authors: Oliver Hellwig
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning, Lemmatization, Morphological Analysis, Morphological Disambiguation

The paper describes a new tagset for the morphological disambiguation of Sanskrit, and compares the accuracy of two machine learning methods (Conditional Random Fields, deep recurrent neural networks) for this task, with a special focus on how to model the lexicographic information. It reports a significant improvement over previously published results.
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters
Authors: Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Community Detection, Information Retrieval, Machine Translation, Multilingual Word Embeddings, Retrieval, Translation, Transliteration, Word Embeddings

In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve. We propose a technique towards solving this problem with the help of multilingual word clusters obtained from multilingual word embeddings. We use word embeddings of the languages projected to a common vector space on which a community-detection algorithm is applied to find clusters such that words that represent the same concept from different languages fall in the same group. We utilize these multilingual word clusters to perform query translation for Cross-Language Information Retrieval for three languages - English, Hindi and Bengali. We have experimented with the FIRE 2012 and Wikipedia datasets and have shown improvements over several standard methods like dictionary-based method, a transliteration-based model and Google Translate.
A study of attention-based neural machine translation model on Indian languages
Authors: Ayan Das, Pranay Yerra, Ken Kumar, Sudeshna Sarkar
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system (Bahdanau et al., 2014) has become the state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we wish to study the performance of the attention-model based NMT system (Bahdanau et al., 2014) on the Indian language pair, Hindi and Bengali, and do an analysis on the types or errors that occur in case when the languages are morphologically rich and there is a scarcity of large parallel training corpus. We then carry out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures that can be carried out.
Comprehensive Part-Of-Speech Tag Set and SVM based POS Tagger for Sinhala
Authors: Fern, S o, areka, Surangika Ranathunga, Sanath Jayasena, Gihan Dias
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: POS, TAG

This paper presents a new comprehensive multi-level Part-Of-Speech tag set and a Support Vector Machine based Part-Of-Speech tagger for the Sinhala language. The currently available tag set for Sinhala has two limitations: the unavailability of tags to represent some word classes and the lack of tags to capture inflection based grammatical variations of words. The new tag set, presented in this paper overcomes both of these limitations. The accuracy of available Sinhala Part-Of-Speech taggers, which are based on Hidden Markov Models, still falls far behind state of the art. Our Support Vector Machine based tagger achieved an overall accuracy of 84.68{\%} with 59.86{\%} accuracy for unknown words and 87.12{\%} for known words, when the test set contains 10{\%} of unknown words.
Align Me: A framework to generate Parallel Corpus Using OCRs and Bilingual Dictionaries
Authors: Priyam Bakliwal, Devadath V V, C. V. Jawahar
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Active Learning, Information Retrieval, Machine Translation, Optical Character Recognition (OCR), Retrieval, Sentence, Translation

Multilingual language processing tasks like statistical machine translation and cross language information retrieval rely mainly on availability of accurate parallel corpora. Manual construction of such corpus can be extremely expensive and time consuming. In this paper we present a simple yet efficient method to generate huge amount of reasonably accurate parallel corpus with minimal user efforts. We utilize the availability of large number of English books and their corresponding translations in other languages to build parallel corpus. Optical Character Recognizing systems are used to digitize such books. We propose a robust dictionary based parallel corpus generation system for alignment of multilingual text at different levels of granularity (sentence, paragraphs, etc). We show the performance of our proposed method on a manually aligned dataset of 300 Hindi-English sentences and 100 English-Malayalam sentences.
Learning Indonesian-Chinese Lexicon with Bilingual Word Embedding Models and Monolingual Signals
Authors: Xinying Qiu, Gangqin Zhu
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Document Classification, Translation, Word Translation

We present a research on learning Indonesian-Chinese bilingual lexicon using monolingual word embedding and bilingual seed lexicons to build shared bilingual word embedding space. We take the first attempt to examine the impact of different monolingual signals for the choice of seed lexicons on the model performance. We found that although monolingual signals alone do not seem to outperform signals coverings all words, the significant improvement for learning word translation of the same signal types may suggest that linguistic features possess value for further study in distinguishing the semantic margins of the shared word embedding space.
Creating rich online dictionaries for the Lao--French language pair, reusable for Machine Translation
Authors: Vincent Berment
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

In this paper, we present how we generated two rich online bilingual dictionaries {---} Lao-French and French-Lao {---} from unstructured dictionaries in Microsoft Word files. Then we shortly discuss the possible reuse of the lexical data for Machine Translation projects.
Information structure, syntax, and pragmatics and other factors in resolving scope ambiguity
Authors: Valentina Apresjan
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: All, Implicatures, Negation

The paper is a corpus study of the factors involved in disambiguating potential scope ambiguity in sentences with negation and universal quantifier, such as {``}I don{'}t want talk to all these people{''}, which can alternatively mean {`}I don{'}t want to talk to any of these people{'} and {`}I don{'}t want to talk to some of these people{'}. The relevant factors are demonstrated to be largely different from those involved in disambiguating lexical polysemy. They include the syntactic function of the constituent containing {``}all{''} quantifier (subject, direct complement, adjunct), as well as the deepness of its embedding; the status of the main predicate and {``}all{''} constituent with respect to the information structure of the 6utterance (topic vs. focus, given vs. new information); pragmatic implicatures pertaining to the situations described in the utterances.
Multiword Expressions at the Grammar-Lexicon Interface
Authors: Timothy Baldwin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

In this talk, I will outline a range of challenges presented by multiword expressions in terms of (lexicalist) precision grammar engineering, and different strategies for accommodating those challenges, in an attempt to strike the right balance in terms of generalisation and over- and under-generation.
Microsyntactic Phenomena as a Computational Linguistics Issue
Authors: Leonid Iomdin
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Position, Question Answering, Text Generation, Translation

Microsyntactic linguistic units, such as syntactic idioms and non-standard syntactic constructions, are poorly represented in linguistic resources, mostly because the former are elements occupying an intermediate position between the lexicon and the grammar and the latter are too specific to be routinely tackled by general grammars. Consequently, many such units produce substantial gaps in systems intended to solve sophisticated computational linguistics tasks, such as parsing, deep semantic analysis, question answering, machine translation, or text generation. They also present obstacles for applying advanced techniques to these tasks, such as machine learning. The paper discusses an approach aimed at bridging such gaps, focusing on the development of monolingual and multilingual corpora where microsyntactic units are to be tagged.
Alternations: From Lexicon to Grammar And Back Again
Authors: Mark{\'e}ta Lopatkov{\'a}, V{\'a}clava Kettnerov{\'a}
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

An excellent example of a phenomenon bridging a lexicon and a grammar is provided by grammaticalized alternations (e.g., passivization, reflexivity, and reciprocity): these alternations represent productive grammatical processes which are, however, lexically determined. While grammaticalized alternations keep lexical meaning of verbs unchanged, they are usually characterized by various changes in their morphosyntactic structure. In this contribution, we demonstrate on the example of reciprocity and its representation in the valency lexicon of Czech verbs, VALLEX how a linguistic description of complex (and still systemic) changes characteristic of grammaticalized alternations can benefit from an integration of grammatical rules into a valency lexicon. In contrast to other types of grammaticalized alternations, reciprocity in Czech has received relatively little attention although it closely interacts with various linguistic phenomena (e.g., with light verbs, diatheses, and reflexivity).
Extra-Specific Multiword Expressions for Language-Endowed Intelligent Agents
Authors: Marjorie McShane, Sergei Nirenburg
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Language-endowed intelligent agents benefit from leveraging lexical knowledge falling at different points along a spectrum of compositionality. This means that robust computational lexicons should include not only the compositional expectations of argument-taking words, but also non-compositional collocations (idioms), semi-compositional collocations that might be difficult for an agent to interpret (e.g., standard metaphors), and even collocations that could be compositionally analyzed but are so frequently encountered that recording their meaning increases the efficiency of interpretation. In this paper we argue that yet another type of string-to-meaning mapping can also be useful to intelligent agents: remembered semantic analyses of actual text inputs. These can be viewed as super-specific multi-word expressions whose recorded interpretations mimic a person{'}s memories of knowledge previously learned from language input. These differ from typical annotated corpora in two ways. First, they provide a full, context-sensitive semantic interpretation rather than select features. Second, they are are formulated in the ontologically-grounded metalanguage used in a particular agent environment, meaning that the interpretations contribute to the dynamically evolving cognitive capabilites of agents configured in that environment.
Universal Dependencies: A Cross-Linguistic Perspective on Grammar and Lexicon
Authors: Joakim Nivre
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Position

Universal Dependencies is an initiative to develop cross-linguistically consistent grammatical annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning and parsing research from a language typology perspective. It assumes a dependency-based approach to syntax and a lexicalist approach to morphology, which together entail that the fundamental units of grammatical annotation are words. Words have properties captured by morphological annotation and enter into relations captured by syntactic annotation. Moreover, priority is given to relations between lexical content words, as opposed to grammatical function words. In this position paper, I discuss how this approach allows us to capture similarities and differences across typologically diverse languages.
The Development of Multimodal Lexical Resources
Authors: James Pustejovsky, Tuan Do, Gitit Kehat, Nikhil Krishnaswamy
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Question Answering, Visual Question Answering (VQA)

Human communication is a multimodal activity, involving not only speech and written expressions, but intonation, images, gestures, visual clues, and the interpretation of actions through perception. In this paper, we describe the design of a multimodal lexicon that is able to accommodate the diverse modalities that present themselves in NLP applications. We have been developing a multimodal semantic representation, VoxML, that integrates the encoding of semantic, visual, gestural, and action-based features associated with linguistic expressions.