Anoop Kunchukuttan


2023

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Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages
Arnav Mhaske | Harshit Kedia | Sumanth Doddapaneni | Mitesh M. Khapra | Pratyush Kumar | Rudra Murthy | Anoop Kunchukuttan
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. The dataset contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location, and, Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language translation. We also create manually annotated testsets for 9 languages. We demonstrate the utility of the obtained dataset on the Naamapadam-test dataset. We also release IndicNER, a multilingual IndicBERT model fine-tuned on Naamapadam training set. IndicNER achieves an F1 score of more than 80 for 7 out of 9 test languages. The dataset and models are available under open-source licences at https://ai4bharat.iitm.ac.in/naamapadam.

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Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages
Sumanth Doddapaneni | Rahul Aralikatte | Gowtham Ramesh | Shreya Goyal | Mitesh M. Khapra | Anoop Kunchukuttan | Pratyush Kumar
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at https://github.com/AI4Bharat/IndicBERT.

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IndicMT Eval: A Dataset to Meta-Evaluate Machine Translation Metrics for Indian Languages
Ananya Sai B | Tanay Dixit | Vignesh Nagarajan | Anoop Kunchukuttan | Pratyush Kumar | Mitesh M. Khapra | Raj Dabre
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The rapid growth of machine translation (MT) systems necessitates meta-evaluations of evaluation metrics to enable selection of those that best reflect MT quality. Unfortunately, most meta-evaluation studies focus on European languages, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from them, and to date, there are no such systematic studies focused solely on English to Indian language MT. This paper fills this gap through a Multidimensional Quality Metric (MQM) dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems. We evaluate 16 metrics and show that, pre-trained metrics like COMET have the highest correlations with annotator scores as opposed to n-gram metrics like BLEU. We further leverage our MQM annotations to develop an Indic-COMET metric and show that it outperforms COMET counterparts in both human scores correlations and robustness scores in Indian languages. Additionally, we show that the Indic-COMET can outperform COMET on some unseen Indian languages. We hope that our dataset and analysis will facilitate further research in Indic MT evaluation.

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Bhasa-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages
Yash Madhani | Mitesh M. Khapra | Anoop Kunchukuttan
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We create publicly available language identification (LID) datasets and models in all 22 Indian languages listed in the Indian constitution in both native-script and romanized text. First, we create Bhasha-Abhijnaanam, a language identification test set for native-script as well as romanized text which spans all 22 Indic languages. We also train IndicLID, a language identifier for all the above-mentioned languages in both native and romanized script. For native-script text, it has better language coverage than existing LIDs and is competitive or better than other LIDs. IndicLID is the first LID for romanized text in Indian languages. Two major challenges for romanized text LID are the lack of training data and low-LID performance when languages are similar. We provide simple and effective solutions to these problems. In general, there has been limited work on romanized text in any language, and our findings are relevant to other languages that need romanized language identification. Our models are publicly available at https://github.com/AI4Bharat/IndicLID under open-source licenses. Our training and test sets are also publicly available at https://huggingface.co/datasets/ai4bharat/Bhasha-Abhijnaanam under open-source licenses.

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Aksharantar: Open Indic-language Transliteration datasets and models for the Next Billion Users
Yash Madhani | Sushane Parthan | Priyanka Bedekar | Gokul Nc | Ruchi Khapra | Anoop Kunchukuttan | Pratyush Kumar | Mitesh Khapra
Findings of the Association for Computational Linguistics: EMNLP 2023

Transliteration is very important in the Indian language context due to the usage of multiple scripts and the widespread use of romanized inputs. However, few training and evaluation sets are publicly available. We introduce Aksharantar, the largest publicly available transliteration dataset for Indian languages created by mining from monolingual and parallel corpora, as well as collecting data from human annotators. The dataset contains 26 million transliteration pairs for 21 Indic languages from 3 language families using 12 scripts. Aksharantar is 21 times larger than existing datasets and is the first publicly available dataset for 7 languages and 1 language family. We also introduce a test set of 103k word pairs for 19 languages that enables a fine-grained analysis of transliteration models on native origin words, foreign words, frequent words, and rare words. Using the training set, we trained IndicXlit, a multilingual transliteration model that improves accuracy by 15% on the Dakshina test set, and establishes strong baselines on the Aksharantar testset introduced in this work. The models, mining scripts, transliteration guidelines, and datasets are available at https://github.com/AI4Bharat/IndicXlit under open-source licenses.

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CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation
Aswanth M | Ratish Puduppully | Raj Dabre | Anoop Kunchukuttan
Findings of the Association for Computational Linguistics: EMNLP 2023

Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples (in-context learning). Translation quality depends on various features of the selected examples, such as their quality and relevance, but previous work has predominantly focused on individual features in isolation. In this paper, we propose a general framework for combining different features influencing example selection. We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality. On multiple language pairs and language models, we show that CTQ Scorer helps significantly outperform random selection as well as strong single-factor baselines reported in the literature. We also see an improvement of over 2.5 COMET points on average with respect to a strong BM25 retrieval-based baseline.

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DecoMT: Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
Ratish Puduppully | Anoop Kunchukuttan | Raj Dabre | Ai Ti Aw | Nancy Chen
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This study investigates machine translation between related languages i.e., languages within the same family that share linguistic characteristics such as word order and lexical similarity. Machine translation through few-shot prompting leverages a small set of translation pair examples to generate translations for test sentences. This procedure requires the model to learn how to generate translations while simultaneously ensuring that token ordering is maintained to produce a fluent and accurate translation. We propose that for related languages, the task of machine translation can be simplified by leveraging the monotonic alignment characteristic of such languages. We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. Through automatic and human evaluation conducted on multiple related language pairs across various language families, we demonstrate that our proposed approach of decomposed prompting surpasses multiple established few-shot baseline approaches. For example, DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.

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Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
Divyanshu Aggarwal | Vivek Gupta | Anoop Kunchukuttan
Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)

Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing. One reason for this imminent gap is the complexity of the logical form, which makes English to multilingual translation difficult. The process involves alignment of logical forms, intents and slots with translated unstructured utterance. To address this, we propose an Inter-bilingual Seq2seq Semantic parsing dataset IE-SemParse Suite for 11 distinct Indian languages. We highlight the proposed task’s practicality, and evaluate existing multilingual seq2seq models across several train-test strategies. Our experiment reveals a high correlation across performance of original multilingual semantic parsing datasets (such as mTOP, multilingual TOP and multiATIS++) and our proposed IE-SemParse suite.

2022

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Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages
Gowtham Ramesh | Sumanth Doddapaneni | Aravinth Bheemaraj | Mayank Jobanputra | Raghavan AK | Ajitesh Sharma | Sujit Sahoo | Harshita Diddee | Mahalakshmi J | Divyanshu Kakwani | Navneet Kumar | Aswin Pradeep | Srihari Nagaraj | Kumar Deepak | Vivek Raghavan | Anoop Kunchukuttan | Pratyush Kumar | Mitesh Shantadevi Khapra
Transactions of the Association for Computational Linguistics, Volume 10

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly available parallel corpora, and additionally mine 37.4 million sentence pairs from the Web, resulting in a 4× increase. We mine the parallel sentences from the Web by combining many corpora, tools, and methods: (a) Web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at Samanantar and we hope they will help advance research in NMT and multilingual NLP for Indic languages.

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IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
Aman Kumar | Himani Shrotriya | Prachi Sahu | Amogh Mishra | Raj Dabre | Ratish Puduppully | Anoop Kunchukuttan | Mitesh M. Khapra | Pratyush Kumar
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages. We present the IndicNLG Benchmark, a collection of datasets for benchmarking NLG for 11 Indic languages. We focus on five diverse tasks, namely, biography generation using Wikipedia infoboxes, news headline generation, sentence summarization, paraphrase generation and, question generation. We describe the created datasets and use them to benchmark the performance of several monolingual and multilingual baselines that leverage pre-trained sequence-to-sequence models. Our results exhibit the strong performance of multilingual language-specific pre-trained models, and the utility of models trained on our dataset for other related NLG tasks. Our dataset creation methods can be easily applied to modest-resource languages as they involve simple steps such as scraping news articles and Wikipedia infoboxes, light cleaning, and pivoting through machine translation data. To the best of our knowledge, the IndicNLG Benchmark is the first NLG benchmark for Indic languages and the most diverse multilingual NLG dataset, with approximately 8M examples across 5 tasks and 11 languages. The datasets and models will be publicly available.

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IndicXNLI: Evaluating Multilingual Inference for Indian Languages
Divyanshu Aggarwal | Vivek Gupta | Anoop Kunchukuttan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

While Indic NLP has made rapid advances recently in terms of the availability of corpora and pre-trained models, benchmark datasets on standard NLU tasks are limited. To this end, we introduce INDICXNLI, an NLI dataset for 11 Indic languages. It has been created by high-quality machine translation of the original English XNLI dataset and our analysis attests to the quality of INDICXNLI. By finetuning different pre-trained LMs on this INDICXNLI, we analyze various cross-lingual transfer techniques with respect to the impact of the choice of language models, languages, multi-linguality, mix-language input, etc. These experiments provide us with useful insights into the behaviour of pre-trained models for a diverse set of languages.

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IndicBART: A Pre-trained Model for Indic Natural Language Generation
Raj Dabre | Himani Shrotriya | Anoop Kunchukuttan | Ratish Puduppully | Mitesh Khapra | Pratyush Kumar
Findings of the Association for Computational Linguistics: ACL 2022

In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller. It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning. Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model.

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Overview of the 9th Workshop on Asian Translation
Toshiaki Nakazawa | Hideya Mino | Isao Goto | Raj Dabre | Shohei Higashiyama | Shantipriya Parida | Anoop Kunchukuttan | Makoto Morishita | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 9th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 9th workshop on Asian translation (WAT2022). For the WAT2022, 8 teams submitted their translation results for the human evaluation. We also accepted 4 research papers. About 300 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Bilingual Tabular Inference: A Case Study on Indic Languages
Chaitanya Agarwal | Vivek Gupta | Anoop Kunchukuttan | Manish Shrivastava
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language. However, due to the uneven distribution of text resources on the web across languages, it is common to have the tabular premise in a high resource language and the hypothesis in a low resource language. As a result, we present the challenging task of bilingual Tabular Natural Language Inference (bTNLI), in which the tabular premise and a hypothesis over it are in two separate languages. We construct EI-InfoTabS: an English-Indic bTNLI dataset by translating the textual hypotheses of the English TNLI dataset InfoTabS into eleven major Indian languages. We thoroughly investigate how pre-trained multilingual models learn and perform on EI-InfoTabS. Our study shows that the performance on bTNLI can be close to its monolingual counterpart, with translate-train, translate-test and unified-train being strongly competitive baselines.

2021

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A Large-scale Evaluation of Neural Machine Transliteration for Indic Languages
Anoop Kunchukuttan | Siddharth Jain | Rahul Kejriwal
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We take up the task of large-scale evaluation of neural machine transliteration between English and Indic languages, with a focus on multilingual transliteration to utilize orthographic similarity between Indian languages. We create a corpus of 600K word pairs mined from parallel translation corpora and monolingual corpora, which is the largest transliteration corpora for Indian languages mined from public sources. We perform a detailed analysis of multilingual transliteration and propose an improved multilingual training recipe for Indic languages. We analyze various factors affecting transliteration quality like language family, transliteration direction and word origin.

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Toshiaki Nakazawa | Hideki Nakayama | Isao Goto | Hideya Mino | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Shohei Higashiyama | Hiroshi Manabe | Win Pa Pa | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Overview of the 8th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Itihasa: A large-scale corpus for Sanskrit to English translation
Rahul Aralikatte | Miryam de Lhoneux | Anoop Kunchukuttan | Anders Søgaard
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This work introduces Itihasa, a large-scale translation dataset containing 93,000 pairs of Sanskrit shlokas and their English translations. The shlokas are extracted from two Indian epics viz., The Ramayana and The Mahabharata. We first describe the motivation behind the curation of such a dataset and follow up with empirical analysis to bring out its nuances. We then benchmark the performance of standard translation models on this corpus and show that even state-of-the-art transformer architectures perform poorly, emphasizing the complexity of the dataset.

2020

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Multilingual Neural Machine Translation
Raj Dabre | Chenhui Chu | Anoop Kunchukuttan
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

The advent of neural machine translation (NMT) has opened up exciting research in building multilingual translation systems i.e. translation models that can handle more than one language pair. Many advances have been made which have enabled (1) improving translation for low-resource languages via transfer learning from high resource languages; and (2) building compact translation models spanning multiple languages. In this tutorial, we will cover the latest advances in NMT approaches that leverage multilingualism, especially to enhance low-resource translation. In particular, we will focus on the following topics: modeling parameter sharing for multi-way models, massively multilingual models, training protocols, language divergence, transfer learning, zero-shot/zero-resource learning, pivoting, multilingual pre-training and multi-source translation.

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Proceedings of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Win Pa Pa | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino | Hiroshi Manabe | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 7th Workshop on Asian Translation

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Overview of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 7th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20
Vikrant Goyal | Anoop Kunchukuttan | Rahul Kejriwal | Siddharth Jain | Amit Bhagwat
Proceedings of the Fifth Conference on Machine Translation

We describe our submission for the English→Tamil and Tamil→English news translation shared task. In this submission, we focus on exploring if a low-resource language (Tamil) can benefit from a high-resource language (Hindi) with which it shares contact relatedness. We show utilizing contact relatedness via multilingual NMT can significantly improve translation quality for English-Tamil translation.

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IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages
Divyanshu Kakwani | Anoop Kunchukuttan | Satish Golla | Gokul N.C. | Avik Bhattacharyya | Mitesh M. Khapra | Pratyush Kumar
Findings of the Association for Computational Linguistics: EMNLP 2020

In this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) pre-trained language models, and (d) multiple NLU evaluation datasets (IndicGLUE benchmark). The monolingual corpora contains a total of 8.8 billion tokens across all 11 languages and Indian English, primarily sourced from news crawls. The word embeddings are based on FastText, hence suitable for handling morphological complexity of Indian languages. The pre-trained language models are based on the compact ALBERT model. Lastly, we compile the (IndicGLUE benchmark for Indian language NLU. To this end, we create datasets for the following tasks: Article Genre Classification, Headline Prediction, Wikipedia Section-Title Prediction, Cloze-style Multiple choice QA, Winograd NLI and COPA. We also include publicly available datasets for some Indic languages for tasks like Named Entity Recognition, Cross-lingual Sentence Retrieval, Paraphrase detection, etc. Our embeddings are competitive or better than existing pre-trained embeddings on multiple tasks. We hope that the availability of the dataset will accelerate Indic NLP research which has the potential to impact more than a billion people. It can also help the community in evaluating advances in NLP over a more diverse pool of languages. The data and models are available at https://indicnlp.ai4bharat.org.

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Learning Geometric Word Meta-Embeddings
Pratik Jawanpuria | Satya Dev N T V | Anoop Kunchukuttan | Bamdev Mishra
Proceedings of the 5th Workshop on Representation Learning for NLP

We propose a geometric framework for learning meta-embeddings of words from different embedding sources. Our framework transforms the embeddings into a common latent space, where, for example, simple averaging or concatenation of different embeddings (of a given word) is more amenable. The proposed latent space arises from two particular geometric transformations - source embedding specific orthogonal rotations and a common Mahalanobis metric scaling. Empirical results on several word similarity and word analogy benchmarks illustrate the efficacy of the proposed framework.

2019

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Proceedings of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Nobushige Doi | Yusuke Oda | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino
Proceedings of the 6th Workshop on Asian Translation

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Overview of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Nobushige Doi | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Yusuke Oda | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 6th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

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Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
Rudra Murthy | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on an assisting language-target language pair (parent model) which is later fine-tuned for the source language-target language pair of interest (child model), with the target language being the same. In many cases, the assisting language has a different word order from the source language. We show that divergent word order adversely limits the benefits from transfer learning when little to no parallel corpus between the source and target language is available. To bridge this divergence, we propose to pre-order the assisting language sentences to match the word order of the source language and train the parent model. Our experiments on many language pairs show that bridging the word order gap leads to significant improvement in the translation quality in extremely low-resource scenarios.

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Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
Pratik Jawanpuria | Arjun Balgovind | Anoop Kunchukuttan | Bamdev Mishra
Transactions of the Association for Computational Linguistics, Volume 7

We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.

2018

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Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
Rudra Murthy | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages. Typically, the goal is improving the NER performance of one of the languages (the primary language) using the other assisting languages. We show that the divergence in the tag distributions of the common named entities between the primary and assisting languages can reduce the effectiveness of multilingual learning. To alleviate this problem, we propose a metric based on symmetric KL divergence to filter out the highly divergent training instances in the assisting language. We empirically show that our data selection strategy improves NER performance in many languages, including those with very limited training data.

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Overview of the 5th Workshop on Asian Translation
Toshiaki Nakazawa | Katsuhito Sudoh | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Sadao Kurohashi
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

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NICT’s Participation in WAT 2018: Approaches Using Multilingualism and Recurrently Stacked Layers
Raj Dabre | Anoop Kunchukuttan | Atsushi Fujita | Eiichiro Sumita
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

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Multilingual Indian Language Translation System at WAT 2018: Many-to-one Phrase-based SMT
Tamali Banerjee | Anoop Kunchukuttan | Pushpak Bhattacharya
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation: 5th Workshop on Asian Translation: 5th Workshop on Asian Translation

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The IIT Bombay English-Hindi Parallel Corpus
Anoop Kunchukuttan | Pratik Mehta | Pushpak Bhattacharyya
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Leveraging Orthographic Similarity for Multilingual Neural Transliteration
Anoop Kunchukuttan | Mitesh Khapra | Gurneet Singh | Pushpak Bhattacharyya
Transactions of the Association for Computational Linguistics, Volume 6

We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration). This is an instance of multitask learning, where individual tasks (language pairs) benefit from sharing knowledge with related tasks. We focus on transliteration involving related tasks i.e., languages sharing writing systems and phonetic properties (orthographically similar languages). We propose a modified neural encoder-decoder model that maximizes parameter sharing across language pairs in order to effectively leverage orthographic similarity. We show that multilingual transliteration significantly outperforms bilingual transliteration in different scenarios (average increase of 58% across a variety of languages we experimented with). We also show that multilingual transliteration models can generalize well to languages/language pairs not encountered during training and hence perform well on the zeroshot transliteration task. We show that further improvements can be achieved by using phonetic feature input.

2017

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Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
Anoop Kunchukuttan | Maulik Shah | Pradyot Prakash | Pushpak Bhattacharyya
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting. We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models. It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used. We also show that combining multiple related language pivot models can rival a direct translation model. Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.

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Learning variable length units for SMT between related languages via Byte Pair Encoding
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task. BPE identifies the most frequent character sequences as basic units, while orthographic syllables are linguistically motivated pseudo-syllables. We show that BPE units modestly outperform orthographic syllables as units of translation, showing up to 11% increase in BLEU score. While orthographic syllables can be used only for languages whose writing systems use vowel representations, BPE is writing system independent and we show that BPE outperforms other units for non-vowel writing systems too. Our results are supported by extensive experimentation spanning multiple language families and writing systems.

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Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation
Sandhya Singh | Ritesh Panjwani | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay’s submission to WAT2017 shared task. We report the results for both English-Hindi and Hindi-English direction of language pair.

2016

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Orthographic Syllable as basic unit for SMT between Related Languages
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Statistical Machine Translation between Related Languages
Pushpak Bhattacharyya | Mitesh M. Khapra | Anoop Kunchukuttan
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

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IIT Bombay’s English-Indonesian submission at WAT: Integrating Neural Language Models with SMT
Sandhya Singh | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)

This paper describes the IIT Bombay’s submission as a part of the shared task in WAT 2016 for English–Indonesian language pair. The results reported here are for both the direction of the language pair. Among the various approaches experimented, Operation Sequence Model (OSM) and Neural Language Model have been submitted for WAT. The OSM approach integrates translation and reordering process resulting in relatively improved translation. Similarly the neural experiment integrates Neural Language Model with Statistical Machine Translation (SMT) as a feature for translation. The Neural Probabilistic Language Model (NPLM) gave relatively high BLEU points for Indonesian to English translation system while the Neural Network Joint Model (NNJM) performed better for English to Indonesian direction of translation system. The results indicate improvement over the baseline Phrase-based SMT by 0.61 BLEU points for English-Indonesian system and 0.55 BLEU points for Indonesian-English translation system.

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Faster Decoding for Subword Level Phrase-based SMT between Related Languages
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

A common and effective way to train translation systems between related languages is to consider sub-word level basic units. However, this increases the length of the sentences resulting in increased decoding time. The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords. In a phrase-based SMT framework, we investigate different choices of decoder parameters as well as data format and their impact on decoding time and translation accuracy. We suggest best options for these settings that significantly improve decoding time with little impact on the translation accuracy.

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Substring-based unsupervised transliteration with phonetic and contextual knowledge
Anoop Kunchukuttan | Pushpak Bhattacharyya | Mitesh M. Khapra
Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning

2015

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Data representation methods and use of mined corpora for Indian language transliteration
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the Fifth Named Entity Workshop

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Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 12th International Conference on Natural Language Processing

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Augmenting Pivot based SMT with word segmentation
Rohit More | Anoop Kunchukuttan | Pushpak Bhattacharyya | Raj Dabre
Proceedings of the 12th International Conference on Natural Language Processing

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Investigating the potential of post-ordering SMT output to improve translation quality
Pratik Mehta | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 12th International Conference on Natural Language Processing

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Brahmi-Net: A transliteration and script conversion system for languages of the Indian subcontinent
Anoop Kunchukuttan | Ratish Puduppully | Pushpak Bhattacharyya
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

2014

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Tuning a Grammar Correction System for Increased Precision
Anoop Kunchukuttan | Sriram Chaudhury | Pushpak Bhattacharyya
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

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The IIT Bombay Hindi-English Translation System at WMT 2014
Piyush Dungarwal | Rajen Chatterjee | Abhijit Mishra | Anoop Kunchukuttan | Ritesh Shah | Pushpak Bhattacharyya
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Supertag Based Pre-ordering in Machine Translation
Rajen Chatterjee | Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the 11th International Conference on Natural Language Processing

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Shata-Anuvadak: Tackling Multiway Translation of Indian Languages
Anoop Kunchukuttan | Abhijit Mishra | Rajen Chatterjee | Ritesh Shah | Pushpak Bhattacharyya
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families. We analyze the relationship between translation accuracy and the language families involved. We feel that insights obtained from this analysis will provide guidelines for creating machine translation systems of specific Indian language pairs. We build phrase based systems and some extensions. Across multiple languages, we show improvements on the baseline phrase based systems using these extensions: (1) source side reordering for English-Indian language translation, and (2) transliteration of untranslated words for Indian language-Indian language translation. These enhancements harness shared characteristics of Indian languages. To stimulate similar innovation widely in the NLP community, we have made the trained models for these language pairs publicly available.

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When Transliteration Met Crowdsourcing : An Empirical Study of Transliteration via Crowdsourcing using Efficient, Non-redundant and Fair Quality Control
Mitesh M. Khapra | Ananthakrishnan Ramanathan | Anoop Kunchukuttan | Karthik Visweswariah | Pushpak Bhattacharyya
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Sufficient parallel transliteration pairs are needed for training state of the art transliteration engines. Given the cost involved, it is often infeasible to collect such data using experts. Crowdsourcing could be a cheaper alternative, provided that a good quality control (QC) mechanism can be devised for this task. Most QC mechanisms employed in crowdsourcing are aggressive (unfair to workers) and expensive (unfair to requesters). In contrast, we propose a low-cost QC mechanism which is fair to both workers and requesters. At the heart of our approach, lies a rule based Transliteration Equivalence approach which takes as input a list of vowels in the two languages and a mapping of the consonants in the two languages. We empirically show that our approach outperforms other popular QC mechanisms (viz., consensus and sampling) on two vital parameters : (i) fairness to requesters (lower cost per correct transliteration) and (ii) fairness to workers (lower rate of rejecting correct answers). Further, as an extrinsic evaluation we use the standard NEWS 2010 test set and show that such quality controlled crowdsourced data compares well to expert data when used for training a transliteration engine.

2013

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TransDoop: A Map-Reduce based Crowdsourced Translation for Complex Domain
Anoop Kunchukuttan | Rajen Chatterjee | Shourya Roy | Abhijit Mishra | Pushpak Bhattacharyya
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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IITB System for CoNLL 2013 Shared Task: A Hybrid Approach to Grammatical Error Correction
Anoop Kunchukuttan | Ritesh Shah | Pushpak Bhattacharyya
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

2012

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Partially modelling word reordering as a sequence labelling problem
Anoop Kunchukuttan | Pushpak Bhattacharyya
Proceedings of the Workshop on Reordering for Statistical Machine Translation

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Experiences in Resource Generation for Machine Translation through Crowdsourcing
Anoop Kunchukuttan | Shourya Roy | Pratik Patel | Kushal Ladha | Somya Gupta | Mitesh M. Khapra | Pushpak Bhattacharyya
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The logistics of collecting resources for Machine Translation (MT) has always been a cause of concern for some of the resource deprived languages of the world. The recent advent of crowdsourcing platforms provides an opportunity to explore the large scale generation of resources for MT. However, before venturing into this mode of resource collection, it is important to understand the various factors such as, task design, crowd motivation, quality control, etc. which can influence the success of such a crowd sourcing venture. In this paper, we present our experiences based on a series of experiments performed. This is an attempt to provide a holistic view of the different facets of translation crowd sourcing and identifying key challenges which need to be addressed for building a practical crowdsourcing solution for MT.
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