Sankaranarayanan Ananthakrishnan


2021

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Knowledge Informed Semantic Parsing for Conversational Question Answering
Raghuveer Thirukovalluru | Mukund Sridhar | Dung Thai | Shruti Chanumolu | Nicholas Monath | Sankaranarayanan Ananthakrishnan | Andrew McCallum
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)

Smart assistants are tasked to answer various questions regarding world knowledge. These questions range from retrieval of simple facts to retrieval of complex, multi-hops question followed by various operators (i.e., filter, argmax). Semantic parsing has emerged as the state-of-the-art for answering these kinds of questions by forming queries to extract information from knowledge bases (KBs). Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers. Yet, NSPs suffer from non-executable logical forms for some instances in the generated logical forms might be missing due to the incompleteness of KBs. Intuitively, knowing the KB structure informs NSP with changes of the global logical forms structures with respect to changes in KB instances. In this work, we propose a novel knowledge-informed decoder variant of NSP. We consider the conversational question answering settings, where a natural language query, its context and its final answers are available at training. Experimental results show that our method outperformed strong baselines by 1.8 F1 points overall across 10 types of questions of the CSQA dataset. Especially for the “Logical Reasoning” category, our model improves by 7 F1 points. Furthermore, our results are achieved with 90.3% fewer parameters, allowing faster training for large-scale datasets.

2014

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Lightly-Supervised Word Sense Translation Error Detection for an Interactive Conversational Spoken Language Translation System
Dennis Mehay | Sankaranarayanan Ananthakrishnan | Sanjika Hewavitharana
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Anticipatory translation model adaptation for bilingual conversations
Sanjika Hewavitharana | Dennis Mehay | Sankaranarayanan Ananthakrishnan | Rohit Kumar | John Makhoul
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Conversational spoken language translation (CSLT) systems facilitate bilingual conversations in which the two participants speak different languages. Bilingual conversations provide additional contextual information that can be used to improve the underlying machine translation system. In this paper, we describe a novel translation model adaptation method that anticipates a participant’s response in the target language, based on his counterpart’s prior turn in the source language. Our proposed strategy uses the source language utterance to perform cross-language retrieval on a large corpus of bilingual conversations in order to obtain a set of potentially relevant target responses. The responses retrieved are used to bias translation choices towards anticipated responses. On an Iraqi-to-English CSLT task, our method achieves a significant improvement over the baseline system in terms of BLEU, TER and METEOR metrics.

2013

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Interactive Error Resolution Strategies for Speech-to-Speech Translation Systems
Rohit Kumar | Matthew Roy | Sankaranarayanan Ananthakrishnan | Sanjika Hewavitharana | Frederick Choi
Proceedings of the SIGDIAL 2013 Conference

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Semi-Supervised Word Sense Disambiguation for Mixed-Initiative Conversational Spoken Language Translation
Sankaranarayanan Ananthakrishnan | Sanjika Hewavitharana | Rohit Kumar | Enoch Kan | Rohit Prasad | Prem Natarajan
Proceedings of Machine Translation Summit XIV: Papers

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Source aware phrase-based decoding for robust conversational spoken language translation
Sankaranarayanan Ananthakrishnan | Wei Chen | Rohit Kumar | Dennis Mehay
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Spoken language translation (SLT) systems typically follow a pipeline architecture, in which the best automatic speech recognition (ASR) hypothesis of an input utterance is fed into a statistical machine translation (SMT) system. Conversational speech often generates unrecoverable ASR errors owing to its rich vocabulary (e.g. out-of-vocabulary (OOV) named entities). In this paper, we study the possibility of alleviating the impact of unrecoverable ASR errors on translation performance by minimizing the contextual effects of incorrect source words in target hypotheses. Our approach is driven by locally-derived penalties applied to bilingual phrase pairs as well as target language model (LM) likelihoods in the vicinity of source errors. With oracle word error labels on an OOV word-rich English-to-Iraqi Arabic translation task, we show statistically significant relative improvements of 3.2% BLEU and 2.0% METEOR over an error-agnostic baseline SMT system. We then investigate the impact of imperfect source error labels on error-aware translation performance. Simulation experiments reveal that modest translation improvements are to be gained with this approach even when the source error labels are noisy.

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Incremental Topic-Based Translation Model Adaptation for Conversational Spoken Language Translation
Sanjika Hewavitharana | Dennis Mehay | Sankaranarayanan Ananthakrishnan | Prem Natarajan
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Active error detection and resolution for speech-to-speech translation
Rohit Prasad | Rohit Kumar | Sankaranarayanan Ananthakrishnan | Wei Chen | Sanjika Hewavitharana | Matthew Roy | Frederick Choi | Aaron Challenner | Enoch Kan | Arvid Neelakantan | Prem Natarajan
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We describe a novel two-way speech-to-speech (S2S) translation system that actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). We present algorithms for detecting out-of-vocabulary (OOV) named entities and terms, sense ambiguities, homophones, idioms, ill-formed input, etc. and discuss novel, interactive strategies for recovering from such errors. We also describe our approach for prioritizing different error types and an extensible architecture for implementing these decisions. We demonstrate the efficacy of our system by presenting analysis on live interactions in the English-to-Iraqi Arabic direction that are designed to invoke different error types for spoken language translation. Our analysis shows that the system can successfully resolve 47% of the errors, resulting in a dramatic improvement in the transfer of problematic concepts.

2011

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System Combination Using Discriminative Cross-Adaptation
Jacob Devlin | Antti-Veikko Rosti | Sankaranarayanan Ananthakrishnan | Spyros Matsoukas
Proceedings of 5th International Joint Conference on Natural Language Processing

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Source Error-Projection for Sample Selection in Phrase-Based SMT for Resource-Poor Languages
Sankaranarayanan Ananthakrishnan | Shiv Vitaladevuni | Rohit Prasad | Prem Natarajan
Proceedings of 5th International Joint Conference on Natural Language Processing

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On-line Language Model Biasing for Statistical Machine Translation
Sankaranarayanan Ananthakrishnan | Rohit Prasad | Prem Natarajan
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Discriminative Sample Selection for Statistical Machine Translation
Sankaranarayanan Ananthakrishnan | Rohit Prasad | David Stallard | Prem Natarajan
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation
Sankaranarayanan Ananthakrishnan | Rohit Prasad | David Stallard | Prem Natarajan
Proceedings of the Fourteenth Conference on Computational Natural Language Learning