Pintu Lohar


2021

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Effective Bitext Extraction From Comparable Corpora Using a Combination of Three Different Approaches
Steinþór Steingrímsson | Pintu Lohar | Hrafn Loftsson | Andy Way
Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)

Parallel sentences extracted from comparable corpora can be useful to supplement parallel corpora when training machine translation (MT) systems. This is even more prominent in low-resource scenarios, where parallel corpora are scarce. In this paper, we present a system which uses three very different measures to identify and score parallel sentences from comparable corpora. We measure the accuracy of our methods in low-resource settings by comparing the results against manually curated test data for English–Icelandic, and by evaluating an MT system trained on the concatenation of the parallel data extracted by our approach and an existing data set. We show that the system is capable of extracting useful parallel sentences with high accuracy, and that the extracted pairs substantially increase translation quality of an MT system trained on the data, as measured by automatic evaluation metrics.

2020

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The Impact of Indirect Machine Translation on Sentiment Classification
Alberto Poncelas | Pintu Lohar | James Hadley | Andy Way
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2019

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Building English-to-Serbian Machine Translation System for IMDb Movie Reviews
Pintu Lohar | Maja Popović | Andy Way
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

This paper reports the results of the first experiment dealing with the challenges of building a machine translation system for user-generated content involving a complex South Slavic language. We focus on translation of English IMDb user movie reviews into Serbian, in a low-resource scenario. We explore potentials and limits of (i) phrase-based and neural machine translation systems trained on out-of-domain clean parallel data from news articles (ii) creating additional synthetic in-domain parallel corpus by machine-translating the English IMDb corpus into Serbian. Our main findings are that morphology and syntax are better handled by the neural approach than by the phrase-based approach even in this low-resource mismatched domain scenario, however the situation is different for the lexical aspect, especially for person names. This finding also indicates that in general, machine translation of person names into Slavic languages (especially those which require/allow transcription) should be investigated more systematically.

2018

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FooTweets: A Bilingual Parallel Corpus of World Cup Tweets
Henny Sluyter-Gäthje | Pintu Lohar | Haithem Afli | Andy Way
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Balancing Translation Quality and Sentiment Preservation (Non-archival Extended Abstract)
Pintu Lohar | Haithem Afli | Andy Way
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

2017

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Using Images to Improve Machine-Translating E-Commerce Product Listings.
Iacer Calixto | Daniel Stein | Evgeny Matusov | Pintu Lohar | Sheila Castilho | Andy Way
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

In this paper we study the impact of using images to machine-translate user-generated e-commerce product listings. We study how a multi-modal Neural Machine Translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attentional NMT and a Statistical Machine Translation (SMT) model. User-generated product listings often do not constitute grammatical or well-formed sentences. More often than not, they consist of the juxtaposition of short phrases or keywords. We train our models end-to-end as well as use text-only and multi-modal NMT models for re-ranking n-best lists generated by an SMT model. We qualitatively evaluate our user-generated training data also analyse how adding synthetic data impacts the results. We evaluate our models quantitatively using BLEU and TER and find that (i) additional synthetic data has a general positive impact on text-only and multi-modal NMT models, and that (ii) using a multi-modal NMT model for re-ranking n-best lists improves TER significantly across different n-best list sizes.

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MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus
Haithem Afli | Pintu Lohar | Andy Way
Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora

Integrating Natural Language Processing (NLP) and computer vision is a promising effort. However, the applicability of these methods directly depends on the availability of a specific multimodal data that includes images and texts. In this paper, we present a collection of a Multimodal corpus of comparable texts and their images in 9 languages from the web news articles of Euronews website. This corpus has found widespread use in the NLP community in Multilingual and multimodal tasks. Here, we focus on its acquisition of the images and text data and their multilingual alignment.

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ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
Pintu Lohar | Koel Dutta Chowdhury | Haithem Afli | Mohammed Hasanuzzaman | Andy Way
Proceedings of the IJCNLP 2017, Shared Tasks

In this age of the digital economy, promoting organisations attempt their best to engage the customers in the feedback provisioning process. With the assistance of customer insights, an organisation can develop a better product and provide a better service to its customer. In this paper, we analyse the real world samples of customer feedback from Microsoft Office customers in four languages, i.e., English, French, Spanish and Japanese and conclude a five-plus-one-classes categorisation (comment, request, bug, complaint, meaningless and undetermined) for meaning classification. The task is to %access multilingual corpora annotated by the proposed meaning categorization scheme and develop a system to determine what class(es) the customer feedback sentences should be annotated as in four languages. We propose following approaches to accomplish this task: (i) a multinomial naive bayes (MNB) approach for multi-label classification, (ii) MNB with one-vs-rest classifier approach, and (iii) the combination of the multilabel classification-based and the sentiment classification-based approach. Our best system produces F-scores of 0.67, 0.83, 0.72 and 0.7 for English, Spanish, French and Japanese, respectively. The results are competitive to the best ones for all languages and secure 3rd and 5th position for Japanese and French, respectively, among all submitted systems.

2016

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The ADAPT Bilingual Document Alignment system at WMT16
Pintu Lohar | Haithem Afli | Chao-Hong Liu | Andy Way
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers