Isabel Trancoso


2020

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Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning
Joana Correia | Isabel Trancoso | Bhiksha Raj
Proceedings of the 12th Language Resources and Evaluation Conference

The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson’s disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube. However, the cost of manually labeling these large datasets can be prohibitive. In this work, we propose to overcome this problem by automating the annotation process, without any requirements for human intervention. We formulate the annotation problem as a Multiple Instance Learning (MIL) problem, and propose a novel solution that is based on end-to-end differentiable neural networks. Our solution has the additional advantage of generalizing the MIL framework to more scenarios where the data is stil organized in bags but does not meet the MIL bag label conditions. We demonstrate the performance of the proposed method in labeling the in-the-Wild Speech Medical (WSM) Corpus, using simple textual cues extracted from videos and their metadata. Furthermore we show what is the contribution of each type of textual cues for the final model performance, as well as study the influence of the size of the bags of instances in determining the difficulty of the learning problem

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Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
André Martins | Helena Moniz | Sara Fumega | Bruno Martins | Fernando Batista | Luisa Coheur | Carla Parra | Isabel Trancoso | Marco Turchi | Arianna Bisazza | Joss Moorkens | Ana Guerberof | Mary Nurminen | Lena Marg | Mikel L. Forcada
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

2018

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Proceedings of the Second Workshop on Subword/Character LEvel Models
Manaal Faruqui | Hinrich Schütze | Isabel Trancoso | Yulia Tsvetkov | Yadollah Yaghoobzadeh
Proceedings of the Second Workshop on Subword/Character LEvel Models

2017

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Proceedings of the First Workshop on Subword and Character Level Models in NLP
Manaal Faruqui | Hinrich Schuetze | Isabel Trancoso | Yadollah Yaghoobzadeh
Proceedings of the First Workshop on Subword and Character Level Models in NLP

2016

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INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words
Silvio Amir | Ramon F. Astudillo | Wang Ling | Mário J. Silva | Isabel Trancoso
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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SPA: Web-based Platform for easy Access to Speech Processing Modules
Fernando Batista | Pedro Curto | Isabel Trancoso | Alberto Abad | Jaime Ferreira | Eugénio Ribeiro | Helena Moniz | David Martins de Matos | Ricardo Ribeiro
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web. It was developed with the aim of facilitating the usage of the modules, without the need to know about software dependencies and specific configurations. Apart from being accessed by a web-browser, the platform also provides a REST API for easy integration with other applications. The platform is flexible, scalable, provides authentication for access restrictions, and was developed taking into consideration the time and effort of providing new services. The platform is still being improved, but it already integrates a considerable number of audio and text processing modules, including: Automatic transcription, speech disfluency classification, emotion detection, dialog act recognition, age and gender classification, non-nativeness detection, hyper-articulation detection, dialog act recognition, and two external modules for feature extraction and DTMF detection. This paper describes the SPA architecture, presents the already integrated modules, and provides a detailed description for the ones most recently integrated.

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Mining Parallel Corpora from Sina Weibo and Twitter
Wang Ling | Luís Marujo | Chris Dyer | Alan W. Black | Isabel Trancoso
Computational Linguistics, Volume 42, Issue 2 - June 2016

2015

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INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction
Silvio Amir | Ramon F. Astudillo | Wang Ling | Bruno Martins | Mario J. Silva | Isabel Trancoso
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces
Ramon F. Astudillo | Silvio Amir | Wang Ling | Bruno Martins | Mario J. Silva | Isabel Trancoso
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Not All Contexts Are Created Equal: Better Word Representations with Variable Attention
Wang Ling | Yulia Tsvetkov | Silvio Amir | Ramón Fermandez | Chris Dyer | Alan W Black | Isabel Trancoso | Chu-Cheng Lin
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Wang Ling | Chris Dyer | Alan W Black | Isabel Trancoso | Ramón Fermandez | Silvio Amir | Luís Marujo | Tiago Luís
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Two/Too Simple Adaptations of Word2Vec for Syntax Problems
Wang Ling | Chris Dyer | Alan W. Black | Isabel Trancoso
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Speech and language technologies for the automatic monitoring and training of cognitive functions
Anna Pompili | Cristiana Amorim | Alberto Abad | Isabel Trancoso
Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces
Ramon F. Astudillo | Silvio Amir | Wang Ling | Mário Silva | Isabel Trancoso
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Automatic Keyword Extraction on Twitter
Luís Marujo | Wang Ling | Isabel Trancoso | Chris Dyer | Alan W. Black | Anatole Gershman | David Martins de Matos | João Neto | Jaime Carbonell
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Toward Better Chinese Word Segmentation for SMT via Bilingual Constraints
Xiaodong Zeng | Lidia S. Chao | Derek F. Wong | Isabel Trancoso | Liang Tian
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Crowdsourcing High-Quality Parallel Data Extraction from Twitter
Wang Ling | Luís Marujo | Chris Dyer | Alan W. Black | Isabel Trancoso
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Revising the annotation of a Broadcast News corpus: a linguistic approach
Vera Cabarrão | Helena Moniz | Fernando Batista | Ricardo Ribeiro | Nuno Mamede | Hugo Meinedo | Isabel Trancoso | Ana Isabel Mata | David Martins de Matos
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a linguistic revision process of a speech corpus of Portuguese broadcast news focusing on metadata annotation for rich transcription, and reports on the impact of the new data on the performance for several modules. The main focus of the revision process consisted on annotating and revising structural metadata events, such as disfluencies and punctuation marks. The resultant revised data is now being extensively used, and was of extreme importance for improving the performance of several modules, especially the punctuation and capitalization modules, but also the speech recognition system, and all the subsequent modules. The resultant data has also been recently used in disfluency studies across domains.

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OpenLogos Semantico-Syntactic Knowledge-Rich Bilingual Dictionaries
Anabela Barreiro | Fernando Batista | Ricardo Ribeiro | Helena Moniz | Isabel Trancoso
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents 3 sets of OpenLogos resources, namely the English-German, the English-French, and the English-Italian bilingual dictionaries. In addition to the usual information on part-of-speech, gender, and number for nouns, offered by most dictionaries currently available, OpenLogos bilingual dictionaries have some distinctive features that make them unique: they contain cross-language morphological information (inflectional and derivational), semantico-syntactic knowledge, indication of the head word in multiword units, information about whether a source word corresponds to an homograph, information about verb auxiliaries, alternate words (i.e., predicate or process nouns), causatives, reflexivity, verb aspect, among others. The focal point of the paper will be the semantico-syntactic knowledge that is important for disambiguation and translation precision. The resources are publicly available at the METANET platform for free use by the research community.

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Linguistic Evaluation of Support Verb Constructions by OpenLogos and Google Translate
Anabela Barreiro | Johanna Monti | Brigitte Orliac | Susanne Preuß | Kutz Arrieta | Wang Ling | Fernando Batista | Isabel Trancoso
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish. We classify support verb constructions by means of their syntactic structure and semantic behavior and present a qualitative analysis of their translation errors. The study aims to verify how machine translation (MT) systems translate fine-grained linguistic phenomena, and how well-equipped they are to produce high-quality translation. Another goal of the linguistically motivated quality analysis of SVC raw output is to reinforce the need for better system hybridization, which leverages the strengths of RBMT to the benefit of SMT, especially in improving the translation of multiword units. Taking multiword units into account, we propose an effective method to achieve MT hybridization based on the integration of semantico-syntactic knowledge into SMT.

2013

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Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT
Longyue Wang | Derek F. Wong | Lidia S. Chao | Junwen Xing | Yi Lu | Isabel Trancoso
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Paraphrasing 4 Microblog Normalization
Wang Ling | Chris Dyer | Alan W Black | Isabel Trancoso
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Microblogs as Parallel Corpora
Wang Ling | Guang Xiang | Chris Dyer | Alan Black | Isabel Trancoso
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Graph-based Semi-Supervised Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Xiaodong Zeng | Derek F. Wong | Lidia S. Chao | Isabel Trancoso
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation
Xiaodong Zeng | Derek F. Wong | Lidia S. Chao | Isabel Trancoso
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Meet EDGAR, a tutoring agent at MONSERRATE
Pedro Fialho | Luísa Coheur | Sérgio Curto | Pedro Cláudio | Ângela Costa | Alberto Abad | Hugo Meinedo | Isabel Trancoso
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Improving Relative-Entropy Pruning using Statistical Significance
Wang Ling | Nadi Tomeh | Guang Xiang | Isabel Trancoso | Alan Black
Proceedings of COLING 2012: Posters

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Entropy-based Pruning for Phrase-based Machine Translation
Wang Ling | João Graça | Isabel Trancoso | Alan Black
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Dealing with unknown words in statistical machine translation
João Silva | Luísa Coheur | Ângela Costa | Isabel Trancoso
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In Statistical Machine Translation, words that were not seen during training are unknown words, that is, words that the system will not know how to translate. In this paper we contribute to this research problem by profiting from orthographic cues given by words. Thus, we report a study of the impact of word distance metrics in cognates' detection and, in addition, on the possibility of obtaining possible translations of unknown words through Logical Analogy. Our approach is tested in the translation of corpora from Portuguese to English (and vice-versa).

2011

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Discriminative Phrase-based Lexicalized Reordering Models using Weighted Reordering Graphs
Wang Ling | João Graça | David Martins de Matos | Isabel Trancoso | Alan W Black
Proceedings of 5th International Joint Conference on Natural Language Processing

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An on-line system for remote treatment of aphasia
Anna Pompili | Alberto Abad | Isabel Trancoso | José Fonseca | Isabel Pavão Martins | Gabriela Leal | Luisa Farrajota
Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies

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Reordering Modeling using Weighted Alignment Matrices
Wang Ling | Tiago Luís | João Graça | Isabel Trancoso | Luísa Coheur
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Named entity translation using anchor texts
Wang Ling | Pável Calado | Bruno Martins | Isabel Trancoso | Alan Black | Luísa Coheur
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

This work describes a process to extract Named Entity (NE) translations from the text available in web links (anchor texts). It translates a NE by retrieving a list of web documents in the target language, extracting the anchor texts from the links to those documents and finding the best translation from the anchor texts, using a combination of features, some of which, are specific to anchor texts. Experiments performed on a manually built corpora, suggest that over 70% of the NEs, ranging from unpopular to popular entities, can be translated correctly using sorely anchor texts. Tests on a Machine Translation task indicate that the system can be used to improve the quality of the translations of state-of-the-art statistical machine translation systems.

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BP2EP - Adaptation of Brazilian Portuguese texts to European Portuguese
Luis Marujo | Nuno Grazina | Tiago Luis | Wang Ling | Luisa Coheur | Isabel Trancoso
Proceedings of the 15th Annual conference of the European Association for Machine Translation

2010

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The INESC-ID machine translation system for the IWSLT 2010
Wang Ling | Tiago Luís | João Graça | Luísa Coheur | Isabel Trancoso
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper we describe the Instituto de Engenharia de Sistemas e Computadores Investigac ̧a ̃o e Desenvolvimento (INESC-ID) system that participated in the IWSLT 2010 evaluation campaign. Our main goal for this evaluation was to employ several state-of-the-art methods applied to phrase-based machine translation in order to improve the translation quality. Aside from the IBM M4 alignment model, two constrained alignment models were tested, which produced better overall results. These results were further improved by using weighted alignment matrixes during phrase extraction, rather than the single best alignment. Finally, we tested several filters that ruled out phrase pairs based on puntuation. Our system was evaluated on the BTEC and DIALOG tasks, having achieved a better overall ranking in the DIALOG task.

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Towards a general and extensible phrase-extraction algorithm
Wang Ling | Tiago Luís | João Graça | Luísa Coheur | Isabel Trancoso
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

Phrase-based systems deeply depend on the quality of their phrase tables and therefore, the process of phrase extraction is always a fundamental step. In this paper we present a general and extensible phrase extraction algorithm, where we have highlighted several control points. The instantiation of these control points allows the simulation of previous approaches, as in each one of these points different strategies/heuristics can be tested. We show how previous approaches fit in this algorithm, compare several of them and, in addition, we propose alternative heuristics, showing their impact on the final translation results. Considering two different test scenarios from the IWSLT 2010 competition (BTEC, Fr-En and DIALOG, Cn-En), we have obtained an improvement in the results of 2.4 and 2.8 BLEU points, respectively.

2008

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Language Dynamics and Capitalization using Maximum Entropy
Fernando Batista | Nuno Mamede | Isabel Trancoso
Proceedings of ACL-08: HLT, Short Papers

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The LECTRA Corpus - Classroom Lecture Transcriptions in European Portuguese
Isabel Trancoso | Rui Martins | Helena Moniz | Ana Isabel Mata | M. Céu Viana
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes the corpus of university lectures that has been recorded in European Portuguese, and some of the recognition experiments we have done with it. The highly specific topic domain and the spontaneous speech nature of the lectures are two of the most challenging problems. Lexical and language model adaptation proved difficult given the scarcity of domain material in Portuguese, but improvements can be achieved with unsupervised acoustic model adaptation. From the point of view of the study of spontaneous speech characteristics, namely disflluencies, the LECTRA corpus has also proved a very valuable resource.

2002

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Morphosyntactic Disambiguation for TTS Systems
Ricardo Ribeiro | Luís Oliveira | Isabel Trancoso
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)