Gaurav Kumar


2021

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Learning Curricula for Multilingual Neural Machine Translation Training
Gaurav Kumar | Philipp Koehn | Sanjeev Khudanpur
Proceedings of Machine Translation Summit XVIII: Research Track

Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula – orderings of the multilingual training data – which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.

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Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora
Gaurav Kumar | Philipp Koehn | Sanjeev Khudanpur
Proceedings of the Sixth Conference on Machine Translation

Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to deal with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.

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TabPert : An Effective Platform for Tabular Perturbation
Nupur Jain | Vivek Gupta | Anshul Rai | Gaurav Kumar
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

To grasp the true reasoning ability, the Natural Language Inference model should be evaluated on counterfactual data. TabPert facilitates this by generation of such counterfactual data for assessing model tabular reasoning issues. TabPert allows the user to update a table, change the hypothesis, change the labels, and highlight rows that are important for hypothesis classification. TabPert also details the technique used to automatically produce the table, as well as the strategies employed to generate the challenging hypothesis. These counterfactual tables and hypotheses, as well as the metadata, is then used to explore the existing model’s shortcomings methodically and quantitatively.

2020

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AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue
Gaurav Kumar | Rishabh Joshi | Jaspreet Singh | Promod Yenigalla
Proceedings of the 12th Language Resources and Evaluation Conference

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture that learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on the next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.

2019

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Curriculum Learning for Domain Adaptation in Neural Machine Translation
Xuan Zhang | Pamela Shapiro | Gaurav Kumar | Paul McNamee | Marine Carpuat | Kevin Duh
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)

We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.

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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
Gaurav Kumar | George Foster | Colin Cherry | Maxim Krikun
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)

We consider the problem of making efficient use of heterogeneous training data in neural machine translation (NMT). Specifically, given a training dataset with a sentence-level feature such as noise, we seek an optimal curriculum, or order for presenting examples to the system during training. Our curriculum framework allows examples to appear an arbitrary number of times, and thus generalizes data weighting, filtering, and fine-tuning schemes. Rather than relying on prior knowledge to design a curriculum, we use reinforcement learning to learn one automatically, jointly with the NMT system, in the course of a single training run. We show that this approach can beat uniform baselines on Paracrawl and WMT English-to-French datasets by +3.4 and +1.3 BLEU respectively. Additionally, we match the performance of strong filtering baselines and hand-designed, state-of-the-art curricula.

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Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Reno Kriz | João Sedoc | Marianna Apidianaki | Carolina Zheng | Gaurav Kumar | Eleni Miltsakaki | Chris Callison-Burch
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)

Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.

2017

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Neural Lattice Search for Domain Adaptation in Machine Translation
Huda Khayrallah | Gaurav Kumar | Kevin Duh | Matt Post | Philipp Koehn
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.

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The JHU Machine Translation Systems for WMT 2017
Shuoyang Ding | Huda Khayrallah | Philipp Koehn | Matt Post | Gaurav Kumar | Kevin Duh
Proceedings of the Second Conference on Machine Translation

2015

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A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation
Gaurav Kumar | Graeme Blackwood | Jan Trmal | Daniel Povey | Sanjeev Khudanpur
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Translations of the Callhome Egyptian Arabic corpus for conversational speech translation
Gaurav Kumar | Yuan Cao | Ryan Cotterell | Chris Callison-Burch | Daniel Povey | Sanjeev Khudanpur
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Translation of the output of automatic speech recognition (ASR) systems, also known as speech translation, has received a lot of research interest recently. This is especially true for programs such as DARPA BOLT which focus on improving spontaneous human-human conversation across languages. However, this research is hindered by the dearth of datasets developed for this explicit purpose. For Egyptian Arabic-English, in particular, no parallel speechtranscription-translation dataset exists in the same domain. In order to support research in speech translation, we introduce the Callhome Egyptian Arabic-English Speech Translation Corpus. This supplements the existing LDC corpus with four reference translations for each utterance in the transcripts. The result is a three-way parallel dataset of Egyptian Arabic Speech, transcriptions and English translations.

2013

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Improved speech-to-text translation with the Fisher and Callhome Spanish-English speech translation corpus
Matt Post | Gaurav Kumar | Adam Lopez | Damianos Karakos | Chris Callison-Burch | Sanjeev Khudanpur
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Research into the translation of the output of automatic speech recognition (ASR) systems is hindered by the dearth of datasets developed for that explicit purpose. For SpanishEnglish translation, in particular, most parallel data available exists only in vastly different domains and registers. In order to support research on cross-lingual speech applications, we introduce the Fisher and Callhome Spanish-English Speech Translation Corpus, supplementing existing LDC audio and transcripts with (a) ASR 1-best, lattice, and oracle output produced by the Kaldi recognition system and (b) English translations obtained on Amazon’s Mechanical Turk. The result is a four-way parallel dataset of Spanish audio, transcriptions, ASR lattices, and English translations of approximately 38 hours of speech, with defined training, development, and held-out test sets. We conduct baseline machine translation experiments using models trained on the provided training data, and validate the dataset by corroborating a number of known results in the field, including the utility of in-domain (information, conversational) training data, increased performance translating lattices (instead of recognizer 1-best output), and the relationship between word error rate and BLEU score.