pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding
Authors: Teruo Hirabayashi, Kanako Komiya, Masayuki Asahara, Hiroyuki Shinnou
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG, Word Embeddings

In this paper, we show how to use bilingual word embeddings (BWE) to automatically create a corresponding table of meaning tags from two dictionaries in one language and examine the effectiveness of the method. To do this, we had a problem: the meaning tags do not always correspond one-to-one because the granularities of the word senses and the concepts are different from each other. Therefore, we regarded the concept tag that corresponds to a word sense the most as the correct concept tag corresponding the word sense. We used two BWE methods, a linear transformation matrix and VecMap. We evaluated the most frequent sense (MFS) method and the corpus concatenation method for comparison. The accuracies of the proposed methods were higher than the accuracy of the random baseline but lower than those of the MFS and corpus concatenation methods. However, because our method utilized the embedding vectors of the word senses, the relations of the sense tags corresponding to concept tags could be examined by mapping the sense embeddings to the vector space of the concept tags. Also, our methods could be performed when we have only concept or word sense embeddings whereas the MFS method requires a parallel corpus and the corpus concatenation method needs two tagged corpora.
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings
Authors: {\v{S}}pela Vintar, Larisa Gr{\v{c}}i{\'c} Simeunovi{\'c}, Matej Martinc, Senja Pollak, Uro{\v{s}} Stepi{\v{s}}nik
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.
Benchmarking Multidomain English-Indonesian Machine Translation
Authors: Tri Wahyu Guntara, Alham Fikri Aji, Radityo Eko Prasojo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Benchmarking, Machine Translation, NMT, Translation

In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language, and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic. In this paper, we show otherwise by collecting large, publicly-available datasets from the Web, which we split into several domains: news, religion, general, and conversation, to train and benchmark some variants of transformer-based NMT models across the domains. We show using BLEU that our models perform well across them , outperform the baseline Statistical Machine Translation (SMT) models, and perform comparably with Google Translate. Our datasets (with the standard split for training, validation, and testing), code, and models are available on \url{https://github.com/gunnxx/indonesian-mt-data}
Reducing the Search Space for Parallel Sentences in Comparable Corpora
Authors: R{\'e}mi Cardon, Natalia Grabar
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

This paper describes and evaluates simple techniques for reducing the research space for parallel sentences in monolingual comparable corpora. Initially, when searching for parallel sentences between two comparable documents, all the possible sentence pairs between the documents have to be considered, which introduces a great degree of imbalance between parallel pairs and non-parallel pairs. This is a problem because even with a high performing algorithm, a lot of noise will be present in the extracted results, thus introducing a need for an extensive and costly manual check phase. We work on a manually annotated subset obtained from a French comparable corpus and show how we can drastically reduce the number of sentence pairs that have to be fed to a classifier so that the results can be manually handled.
LMU Bilingual Dictionary Induction System with Word Surface Similarity Scores for BUCC 2020
Authors: Silvia Severini, Viktor Hangya, Alex Fraser, er, Hinrich Sch{\"u}tze
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation, Word Embeddings, Word Similarity

The task of Bilingual Dictionary Induction (BDI) consists of generating translations for source language words which is important in the framework of machine translation (MT). The aim of the BUCC 2020 shared task is to perform BDI on various language pairs using comparable corpora. In this paper, we present our approach to the task of English-German and English-Russian language pairs. Our system relies on Bilingual Word Embeddings (BWEs) which are often used for BDI when only a small seed lexicon is available making them particularly effective in a low-resource setting. On the other hand, they perform well on high frequency words only. In order to improve the performance on rare words as well, we combine BWE based word similarity with word surface similarity methods, such as orthography In addition to the often used top-n translation method, we experiment with a margin based approach aiming for dynamic number of translations for each source word. We participate in both the open and closed tracks of the shared task and we show improved results of our method compared to simple vector similarity based approaches. Our system was ranked in the top-3 teams and achieved the best results for English-Russian.
TALN/LS2N Participation at the BUCC Shared Task: Bilingual Dictionary Induction from Comparable Corpora
Authors: Martin Laville, Amir Hazem, Emmanuel Morin
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper describes the TALN/LS2N system participation at the Building and Using Comparable Corpora (BUCC) shared task. We first introduce three strategies: (i) a word embedding approach based on fastText embeddings; (ii) a concatenation approach using both character Skip-Gram and character CBOW models, and finally (iii) a cognates matching approach based on an exact match string similarity. Then, we present the applied strategy for the shared task which consists in the combination of the embeddings concatenation and the cognates matching approaches. The covered languages are French, English, German, Russian and Spanish. Overall, our system mixing embeddings concatenation and perfect cognates matching obtained the best results while compared to individual strategies, except for English-Russian and Russian-English language pairs for which the concatenation approach was preferred.
cEnTam: Creation and Validation of a New English-Tamil Bilingual Corpus
Authors: Sanjanasri JP, Premjith B, Vijay Krishna Menon, Soman Kp
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Machine Translation, Retrieval, Translation

Natural Language Processing (NLP), is the field of artificial intelligence that gives the computer the ability to interpret, perceive and extract appropriate information from human languages. Contemporary NLP is predominantly a data driven process. It employs machine learning and statistical algorithms to learn language structures from textual corpus. While application of NLP in English, certain European languages such as Spanish, German, etc. and Chinese, Arabic has been tremendous, it is not so, in many Indian languages. There are obvious advantages in creating aligned bilingual and multilingual corpora. Machine translation, cross-lingual information retrieval, content availability and linguistic comparison are a few of the most sought after applications of such parallel corpora. This paper explains and validates a parallel corpus we created for English-Tamil bilingual pair.
BUCC2020: Bilingual Dictionary Induction using Cross-lingual Embedding
Authors: Sanjanasri JP, Vijay Krishna Menon, Soman KP
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Deep Learning, Word Embeddings

This paper presents a deep learning system for the BUCC 2020 shared task: Bilingual dictionary induction from comparable corpora. We have submitted two runs for this shared Task, German (de) and English (en) language pair for {``}closed track{''} and Tamil (ta) and English (en) for the {``}open track{''}. Our core approach focuses on quantifying the semantics of the language pairs, so that semantics of two different language pairs can be compared or transfer learned. With the advent of word embeddings, it is possible to quantify this. In this paper, we propose a deep learning approach which makes use of the supplied training data, to generate cross-lingual embedding. This is later used for inducting bilingual dictionary from comparable corpora.
An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines
Authors: Elena Alvarez-Mellado
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The extraction of anglicisms (lexical borrowings from English) is relevant both for lexicographic purposes and for NLP downstream tasks. We introduce a corpus of European Spanish newspaper headlines annotated with anglicisms and a baseline model for anglicism extraction. In this paper we present: (1) a corpus of 21,570 newspaper headlines written in European Spanish annotated with emergent anglicisms and (2) a conditional random field baseline model with handcrafted features for anglicism extraction. We present the newspaper headlines corpus, describe the annotation tagset and guidelines and introduce a CRF model that can serve as baseline for the task of detecting anglicisms. The presented work is a first step towards the creation of an anglicism extractor for Spanish newswire.
When is Multi-task Learning Beneficial for Low-Resource Noisy Code-switched User-generated Algerian Texts?
Authors: Wafia Adouane, Jean-Philippe Bernardy
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Data Augmentation, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

We investigate when is it beneficial to simultaneously learn representations for several tasks, in low-resource settings. For this, we work with noisy user-generated texts in Algerian, a low-resource non-standardised Arabic variety. That is, to mitigate the problem of the data scarcity, we experiment with jointly learning progressively 4 tasks, namely code-switch detection, named entity recognition, spell normalisation and correction, and identifying users{'} sentiments. The selection of these tasks is motivated by the lack of labelled data for automatic morpho-syntactic or semantic sequence-tagging tasks for Algerian, in contrast to the case of much multi-task learning for NLP. Our empirical results show that multi-task learning is beneficial for some tasks in particular settings, and that the effect of each task on another, the order of the tasks, and the size of the training data of the task with more data do matter. Moreover, the data augmentation that we performed with no external resources has been shown to be beneficial for certain tasks.
Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data
Authors: Arra{'}Di Nur Rizal, Sara Stymne
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis, Word Embeddings

Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora. In this paper, we explore and evaluate different types of word embeddings for Indonesian{--}English code-mixed text. We propose the use of code-mixed embeddings, i.e. embeddings trained on code-mixed text. Because large corpora of code-mixed text are required to train embeddings, we describe a method for synthesizing a code-mixed corpus, grounded in literature and a survey. Using sentiment analysis as a case study, we show that code-mixed embeddings trained on synthesized data are at least as good as cross-lingual embeddings and better than monolingual embeddings.
Understanding Script-Mixing: A Case Study of Hindi-English Bilingual Twitter Users
Authors: Abhishek Srivastava, Kalika Bali, Monojit Choudhury
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

In a multi-lingual and multi-script society such as India, many users resort to code-mixing while typing on social media. While code-mixing has received a lot of attention in the past few years, it has mostly been studied within a single-script scenario. In this work, we present a case study of Hindi-English bilingual Twitter users while considering the nuances that come with the intermixing of different scripts. We present a concise analysis of how scripts and languages interact in communities and cultures where code-mixing is rampant and offer certain insights into the findings. Our analysis shows that both intra-sentential and inter-sentential script-mixing are present on Twitter and show different behavior in different contexts. Examples suggest that script can be employed as a tool for emphasizing certain phrases within a sentence or disambiguating the meaning of a word. Script choice can also be an indicator of whether a word is borrowed or not. We present our analysis along with examples that bring out the nuances of the different cases.
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings
Authors: Pranaydeep Singh, Els Lefever
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Cross-Lingual Word Embeddings, Sentiment Analysis, Transfer Learning, Word Embeddings

This paper investigates the use of unsupervised cross-lingual embeddings for solving the problem of code-mixed social media text understanding. We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz. English combined with (transliterated) Hindi. In a first step, baseline models, initialized with monolingual embeddings obtained from large collections of tweets in English and code-mixed Hinglish, were trained. In a second step, two systems using cross-lingual embeddings were researched, being (1) a supervised classifier and (2) a transfer learning approach trained on English sentiment data and evaluated on code-mixed data. We demonstrate that incorporating cross-lingual embeddings improves the results (F1-score of 0.635 versus a monolingual baseline of 0.616), without any parallel data required to train the cross-lingual embeddings. In addition, the results show that the cross-lingual embeddings not only improve the results in a fully supervised setting, but they can also be used as a base for distant supervision, by training a sentiment model in one of the source languages and evaluating on the other language projected in the same space. The transfer learning experiments result in an F1-score of 0.556, which is almost on par with the supervised settings and speak to the robustness of the cross-lingual embeddings approach.
Semi-supervised acoustic and language model training for English-isiZulu code-switched speech recognition
Authors: Astik Biswas, Febe De Wet, Ewald van der Westhuizen, Thomas Niesler
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We present an analysis of semi-supervised acoustic and language model training for English-isiZulu code-switched (CS) ASR using soap opera speech. Approximately 11 hours of untranscribed multilingual speech was transcribed automatically using four bilingual CS transcription systems operating in English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho. These transcriptions were incorporated into the acoustic and language model training sets. Results showed that the TDNN-F acoustic models benefit from the additional semi-supervised data and that even better performance could be achieved by including additional CNN layers. Using these CNN-TDNN-F acoustic models, a first iteration of semi-supervised training achieved an absolute mixed-language WER reduction of 3.44{\%}, and a further 2.18{\%} after a second iteration. Although the languages in the untranscribed data were unknown, the best results were obtained when all automatically transcribed data was used for training and not just the utterances classified as English-isiZulu. Despite perplexity improvements, the semi-supervised language model was not able to improve the ASR performance.
Code-mixed parse trees and how to find them
Authors: Anirudh Srinivasan, D, S apat, ipan, Monojit Choudhury
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into {``}pseudo constituency trees{''} and find that a parser trained on synthetically generated trees is able to decently parse these as well.
Towards an Efficient Code-Mixed Grapheme-to-Phoneme Conversion in an Agglutinative Language: A Case Study on To-Korean Transliteration
Authors: Won Ik Cho, Seok Min Kim, Nam Soo Kim
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Grapheme-to-Phoneme Conversion, Philosophy, Sentence, speech-recognition, Speech Recognition, Transliteration

Code-mixed grapheme-to-phoneme (G2P) conversion is a crucial issue for modern speech recognition and synthesis task, but has been seldom investigated in sentence-level in literature. In this study, we construct a system that performs precise and efficient multi-stage code-mixed G2P conversion, for a less studied agglutinative language, Korean. The proposed system undertakes a sentence-level transliteration that is effective in the accurate processing of Korean text. We formulate the underlying philosophy that supports our approach and demonstrate how it fits with the contemporary document.
LanguageARC: Developing Language Resources Through Citizen Linguistics
Authors: James Fiumara, Christopher Cieri, Jonathan Wright, Mark Liberman
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: ARC

This paper introduces the citizen science platform, LanguageARC, developed within the NIEUW (Novel Incentives and Workflows) project supported by the National Science Foundation under Grant No. 1730377. LanguageARC is a community-oriented online platform bringing together researchers and {``}citizen linguists{''} with the shared goal of contributing to linguistic research and language technology development. Like other Citizen Science platforms and projects, LanguageARC harnesses the power and efforts of volunteers who are motivated by the incentives of contributing to science, learning and discovery, and belonging to a community dedicated to social improvement. Citizen linguists contribute language data and judgments by participating in research tasks such as classifying regional accents from audio clips, recording audio of picture descriptions and answering personality questionnaires to create baseline data for NLP research into autism and neurodegenerative conditions. Researchers can create projects on Language ARC without any coding or HTML required using our Project Builder Toolkit.
Developing Language Resources with Citizen Linguistics in Austria -- A Case Study
Authors: Barbara Heinisch
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation

Language resources are a major ingredient for the advancement of language technologies. Citizen linguistics can help to create language resources and annotate language resources, not only for the improvement of language technologies, such as machine translation but also for the advancement of linguistic research. The (language) resources covered in this article are a corpus related to the Question of the Month project strand, which was initially aimed at co-creation in citizen linguistics and a partially annotated database of pictures of written text in different languages found in the public sphere. The number of participants in these project strands differed significantly. Especially those activities that were related to data collection (and analysis) had a significantly higher number of contributions per participant. This especially held true for the activities with (prize) incentives. Nevertheless, the activities of the Question of the Month could reach a higher number of participants, even after the co-creation approach was no longer followed. In addition, the Question of the Month brought research gaps and new knowledge to light and challenged existing paradigms and practices. These are especially important for the advancement of scholarly research. Citizen linguistics can help gather and analyze linguistic data, including language resources, in a short period of time. Thus, it may help increase the access to and availability of language resources.
Objective Assessment of Subjective Tasks in Crowdsourcing Applications
Authors: Giannis Haralabopoulos, Tsik, Myron ilakis, Mercedes Torres Torres, Derek McAuley
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis

Labelling, or annotation, is the process by which we assign labels to an item with regards to a task. In some Artificial Intelligence problems, such as Computer Vision tasks, the goal is to obtain objective labels. However, in problems such as text and sentiment analysis, subjective labelling is often required. More so when the sentiment analysis deals with actual emotions instead of polarity (positive/negative) . Scientists employ human experts to create these labels, but it is costly and time consuming. Crowdsourcing enables researchers to utilise non-expert knowledge for scientific tasks. From image analysis to semantic annotation, interested researchers can gather a large sample of answers via crowdsourcing platforms in a timely manner. However, non-expert contributions often need to be thoroughly assessed, particularly so when a task is subjective. Researchers have traditionally used {`}Gold Standard{'}, {`}Thresholding{'} and {`}Majority Voting{'} as methods to filter non-expert contributions. We argue that these methods are unsuitable for subjective tasks, such as lexicon acquisition and sentiment analysis. We discuss subjectivity in human centered tasks and present a filtering method that defines quality contributors, based on a set of objectively infused terms in a lexicon acquisition task. We evaluate our method against an established lexicon, the diversity of emotions - i.e. subjectivity- and the exclusion of contributions. Our proposed objective evaluation method can be used to assess contributors in subjective tasks that will provide domain agnostic, quality results, with at least 7{\%} improvement over traditional methods.