Gonzalo Iglesias


2023

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An Inner Table Retriever for Robust Table Question Answering
Weizhe Lin | Rexhina Blloshmi | Bill Byrne | Adria de Gispert | Gonzalo Iglesias
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent years have witnessed the thriving of pretrained Transformer-based language models for understanding semi-structured tables, with several applications, such as Table Question Answering (TableQA).These models are typically trained on joint tables and surrounding natural language text, by linearizing table content into sequences comprising special tokens and cell information. This yields very long sequences which increase system inefficiency, and moreover, simply truncating long sequences results in information loss for downstream tasks. We propose Inner Table Retriever (ITR), a general-purpose approach for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question.We show that ITR can be easily integrated into existing systems to improve their accuracy with up to 1.3-4.8% and achieve state-of-the-art results in two benchmarks, i.e., 63.4% in WikiTableQuestions and 92.1% in WikiSQL. Additionally, we show that ITR makes TableQA systems more robust to reduced model capacity and to different ordering of columns and rows. We make our code available at: https://github.com/amazon-science/robust-tableqa.

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LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering
Weizhe Lin | Rexhina Blloshmi | Bill Byrne | Adria de Gispert | Gonzalo Iglesias
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Recent open-domain TableQA models are typically implemented as retriever-reader pipelines. The retriever component is usually a variant of the Dense Passage Retriever, which computes the similarities between questions and tables based on a single representation of each.These fixed vectors can be insufficient to capture fine-grained features of potentially very big tables with heterogeneous row/column information. We address this limitation by 1) applying late interaction models which enforce a finer-grained interaction between question and table embeddings at retrieval time. In addition, we 2) incorporate a joint training scheme of the retriever and reader with explicit table-level signals, and 3) embed a binary relevance token as a prefix to the answer generated by the reader, so we can determine at inference time whether the table used to answer the question is reliable and filter accordingly. The combined strategies set a new state-to-the-art performance on two public open-domain TableQA datasets.

2018

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Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
Felix Stahlberg | Danielle Saunders | Gonzalo Iglesias | Bill Byrne
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Neural Machine Translation Decoding with Terminology Constraints
Eva Hasler | Adrià de Gispert | Gonzalo Iglesias | Bill Byrne
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.

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Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Gonzalo Iglesias | William Tambellini | Adrià De Gispert | Eva Hasler | Bill Byrne
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers. We also discuss acceleration strategies for deployment, and the effect of the beam size and batching on memory and speed.

2016

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Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
Daniel Beck | Adrià de Gispert | Gonzalo Iglesias | Aurelien Waite | Bill Byrne
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Fast and Accurate Preordering for SMT using Neural Networks
Adrià de Gispert | Gonzalo Iglesias | Bill Byrne
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Transducer Disambiguation with Sparse Topological Features
Gonzalo Iglesias | Adrià de Gispert | Bill Byrne
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Pushdown Automata in Statistical Machine Translation
Cyril Allauzen | Bill Byrne | Adrià de Gispert | Gonzalo Iglesias | Michael Riley
Computational Linguistics, Volume 40, Issue 3 - September 2014

2012

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Can Automatic Post-Editing Make MT More Meaningful
Kristen Parton | Nizar Habash | Kathleen McKeown | Gonzalo Iglesias | Adrià de Gispert
Proceedings of the 16th Annual Conference of the European Association for Machine Translation

2011

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Hierarchical Phrase-based Translation Representations
Gonzalo Iglesias | Cyril Allauzen | William Byrne | Adrià de Gispert | Michael Riley
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Hierarchical Phrase-Based Translation with Weighted Finite-State Transducers and Shallow-n Grammars
Adrià de Gispert | Gonzalo Iglesias | Graeme Blackwood | Eduardo R. Banga | William Byrne
Computational Linguistics, Volume 36, Issue 3 - September 2010

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The CUED HiFST System for the WMT10 Translation Shared Task
Juan Pino | Gonzalo Iglesias | Adrià de Gispert | Graeme Blackwood | Jamie Brunning | William Byrne
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

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Hierarchical Phrase-Based Translation with Weighted Finite State Transducers
Gonzalo Iglesias | Adrià de Gispert | Eduardo R. Banga | William Byrne
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Rule Filtering by Pattern for Efficient Hierarchical Translation
Gonzalo Iglesias | Adrià de Gispert | Eduardo R. Banga | William Byrne
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)