Kashif Shah


2021

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Interpreting Text Classifiers by Learning Context-sensitive Influence of Words
Sawan Kumar | Kalpit Dixit | Kashif Shah
Proceedings of the First Workshop on Trustworthy Natural Language Processing

Many existing approaches for interpreting text classification models focus on providing importance scores for parts of the input text, such as words, but without a way to test or improve the interpretation method itself. This has the effect of compounding the problem of understanding or building trust in the model, with the interpretation method itself adding to the opacity of the model. Further, importance scores on individual examples are usually not enough to provide a sufficient picture of model behavior. To address these concerns, we propose MOXIE (MOdeling conteXt-sensitive InfluencE of words) with an aim to enable a richer interface for a user to interact with the model being interpreted and to produce testable predictions. In particular, we aim to make predictions for importance scores, counterfactuals and learned biases with MOXIE. In addition, with a global learning objective, MOXIE provides a clear path for testing and improving itself. We evaluate the reliability and efficiency of MOXIE on the task of sentiment analysis.

2018

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Neural Network based Extreme Classification and Similarity Models for Product Matching
Kashif Shah | Selcuk Kopru | Jean-David Ruvini
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

Matching a seller listed item to an appropriate product has become a fundamental and one of the most significant step for e-commerce platforms for product based experience. It has a huge impact on making the search effective, search engine optimization, providing product reviews and product price estimation etc. along with many other advantages for a better user experience. As significant and vital it has become, the challenge to tackle the complexity has become huge with the exponential growth of individual and business sellers trading millions of products everyday. We explored two approaches; classification based on shallow neural network and similarity based on deep siamese network. These models outperform the baseline by more than 5% in term of accuracy and are capable of extremely efficient training and inference.

2016

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SHEF-Multimodal: Grounding Machine Translation on Images
Kashif Shah | Josiah Wang | Lucia Specia
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Word embeddings and discourse information for Quality Estimation
Carolina Scarton | Daniel Beck | Kashif Shah | Karin Sim Smith | Lucia Specia
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features
Kashif Shah | Fethi Bougares | Loïc Barrault | Lucia Specia
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box
Ahmet Aker | Frederic Blain | Andres Duque | Marina Fomicheva | Jurica Seva | Kashif Shah | Daniel Beck
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Large-scale Multitask Learning for Machine Translation Quality Estimation
Kashif Shah | Lucia Specia
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Creation of comparable corpora for English-Urdu, Arabic, Persian
Murad Abouammoh | Kashif Shah | Ahmet Aker
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora. However, in the case of under-resourced languages or some specific domains, parallel corpora are not readily available. This leads to under-performing machine translation systems in those sparse data settings. To overcome the low availability of parallel resources the machine translation community has recognized the potential of using comparable resources as training data. However, most efforts have been related to European languages and less in middle-east languages. In this study, we report comparable corpora created from news articles for the pair English ―{Arabic, Persian, Urdu} languages. The data has been collected over a period of a year, entails Arabic, Persian and Urdu languages. Furthermore using the English as a pivot language, comparable corpora that involve more than one language can be created, e.g. English- Arabic - Persian, English - Arabic - Urdu, English ― Urdu - Persian, etc. Upon request the data can be provided for research purposes.

2015

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Investigating Continuous Space Language Models for Machine Translation Quality Estimation
Kashif Shah | Raymond W. M. Ng | Fethi Bougares | Lucia Specia
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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SHEF-NN: Translation Quality Estimation with Neural Networks
Kashif Shah | Varvara Logacheva | Gustavo Paetzold | Frederic Blain | Daniel Beck | Fethi Bougares | Lucia Specia
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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Predicting human translation quality
Lucia Specia | Kashif Shah
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

We present a first attempt at predicting the quality of translations produced by human, professional translators. We examine datasets annotated for quality at sentence- and word-level for four language pairs and provide experiments with prediction models for these datasets. We compare the performance of such models against that of models built from machine translations, highlighting a number of challenges in estimating quality and detecting errors in human translations.

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QuEst: A framework for translation quality estimation
Lucia Specia | Kashif Shah
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas

We present QUEST, an open source framework for translation quality estimation. QUEST provides a wide range of feature extractors from source and translation texts and external resources and tools. These go from simple, language-independent features, to advanced, linguistically motivated features. They include features that rely on information from the translation system and features that are oblivious to the way translations were produced. In addition, it provides wrappers for a well-known machine learning toolkit, scikit-learn, including techniques for feature selection and model building, as well as parameter optimisation. We also present a Web interface and functionalities for non-expert users. Using this interface, quality predictions (or internal features of the framework) can be obtained without the installation of the toolkit and the building of prediction models. The interface also provides a ranking method for multiple translations given for the same source text according to their predicted quality.

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The USFD SLT system for IWSLT 2014
Raymond W. M. Ng | Mortaza Doulaty | Rama Doddipatla | Wilker Aziz | Kashif Shah | Oscar Saz | Madina Hasan | Ghada AlHaribi | Lucia Specia | Thomas Hain
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techniques. Machine translation (MT) is achieved by a phrase-based system. The USFD primary system incorporates state-of-the-art ASR and MT techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French and English-to-German speech-to-text translation task with the IWSLT 2014 data. The USFD contrastive systems explore the integration of ASR and MT by using a quality estimation system to rescore the ASR outputs, optimising towards better translation. This gives a further 0.54 and 0.26 BLEU improvement respectively on the IWSLT 2012 and 2014 evaluation data.

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An efficient and user-friendly tool for machine translation quality estimation
Kashif Shah | Marco Turchi | Lucia Specia
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a new version of QUEST ― an open source framework for machine translation quality estimation ― which brings a number of improvements: (i) it provides a Web interface and functionalities such that non-expert users, e.g. translators or lay-users of machine translations, can get quality predictions (or internal features of the framework) for translations without having to install the toolkit, obtain resources or build prediction models; (ii) it significantly improves over the previous runtime performance by keeping resources (such as language models) in memory; (iii) it provides an option for users to submit the source text only and automatically obtain translations from Bing Translator; (iv) it provides a ranking of multiple translations submitted by users for each source text according to their estimated quality. We exemplify the use of this new version through some experiments with the framework.

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SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation
Daniel Beck | Kashif Shah | Lucia Specia
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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QuEst - A translation quality estimation framework
Lucia Specia | Kashif Shah | Jose G.C. de Souza | Trevor Cohn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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SHEF-Lite: When Less is More for Translation Quality Estimation
Daniel Beck | Kashif Shah | Trevor Cohn | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

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An Investigation on the Effectiveness of Features for Translation Quality Estimation
Kashif Shah | Trevor Conn | Lucia Specia
Proceedings of Machine Translation Summit XIV: Papers

2012

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A General Framework to Weight Heterogeneous Parallel Data for Model Adaptation in Statistical MT
Kashif Shah | Loïc Barrault | Holger Schwenk
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

The standard procedure to train the translation model of a phrase-based SMT system is to concatenate all available parallel data, to perform word alignment, to extract phrase pairs and to calculate translation probabilities by simple relative frequency. However, parallel data is quite inhomogeneous in many practical applications with respect to several factors like data source, alignment quality, appropriateness to the task, etc. We propose a general framework to take into account these factors during the calculation of the phrase-table, e.g. by better distributing the probability mass of the individual phrase pairs. No additional feature functions are needed. We report results on two well-known tasks: the IWSLT’11 and WMT’11 evaluations, in both conditions translating from English to French. We give detailed results for different functions to weight the bitexts. Our best systems improve a strong baseline by up to one BLEU point without any impact on the computational complexity during training or decoding.

2011

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Parametric Weighting of Parallel Data for Statistical Machine Translation
Kashif Shah | Loïc Barrault | Holger Schwenk
Proceedings of 5th International Joint Conference on Natural Language Processing

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LIUM’s SMT Machine Translation Systems for WMT 2011
Holger Schwenk | Patrik Lambert | Loïc Barrault | Christophe Servan | Sadaf Abdul-Rauf | Haithem Afli | Kashif Shah
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Translation Model Adaptation by Resampling
Kashif Shah | Loïc Barrault | Holger Schwenk
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR