Luisa Bentivogli


2022

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Post-editing in Automatic Subtitling: A Subtitlers’ perspective
Alina Karakanta | Luisa Bentivogli | Mauro Cettolo | Matteo Negri | Marco Turchi
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Recent developments in machine translation and speech translation are opening up opportunities for computer-assisted translation tools with extended automation functions. Subtitling tools are recently being adapted for post-editing by providing automatically generated subtitles, and featuring not only machine translation, but also automatic segmentation and synchronisation. But what do professional subtitlers think of post-editing automatically generated subtitles? In this work, we conduct a survey to collect subtitlers’ impressions and feedback on the use of automatic subtitling in their workflows. Our findings show that, despite current limitations stemming mainly from speech processing errors, automatic subtitling is seen rather positively and has potential for the future.

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Towards a methodology for evaluating automatic subtitling
Alina Karakanta | Luisa Bentivogli | Mauro Cettolo | Matteo Negri | Marco Turchi
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

In response to the increasing interest towards automatic subtitling, this EAMT-funded project aimed at collecting subtitle post-editing data in a real use case scenario where professional subtitlers edit automatically generated subtitles. The post-editing setting includes, for the first time, automatic generation of timestamps and segmentation, and focuses on the effect of timing and segmentation edits on the post-editing process. The collected data will serve as the basis for investigating how subtitlers interact with automatic subtitling and for devising evaluation methods geared to the multimodal nature and formal requirements of subtitling.

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Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology
Luisa Bentivogli | Mauro Cettolo | Marco Gaido | Alina Karakanta | Matteo Negri | Marco Turchi
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This project aimed at extending the test sets of the MuST-C speech translation (ST) corpus with new reference translations. The new references were collected from professional post-editors working on the output of different ST systems for three language pairs: English-German/Italian/Spanish. In this paper, we shortly describe how the data were collected and how they are distributed. As an evidence of their usefulness, we also summarise the findings of the first comparative evaluation of cascade and direct ST approaches, which was carried out relying on the collected data. The project was partially funded by the European Association for Machine Translation (EAMT) through its 2020 Sponsorship of Activities programme.

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Findings of the IWSLT 2022 Evaluation Campaign
Antonios Anastasopoulos | Loïc Barrault | Luisa Bentivogli | Marcely Zanon Boito | Ondřej Bojar | Roldano Cattoni | Anna Currey | Georgiana Dinu | Kevin Duh | Maha Elbayad | Clara Emmanuel | Yannick Estève | Marcello Federico | Christian Federmann | Souhir Gahbiche | Hongyu Gong | Roman Grundkiewicz | Barry Haddow | Benjamin Hsu | Dávid Javorský | Vĕra Kloudová | Surafel Lakew | Xutai Ma | Prashant Mathur | Paul McNamee | Kenton Murray | Maria Nǎdejde | Satoshi Nakamura | Matteo Negri | Jan Niehues | Xing Niu | John Ortega | Juan Pino | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Yogesh Virkar | Alexander Waibel | Changhan Wang | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

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Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation
Beatrice Savoldi | Marco Gaido | Luisa Bentivogli | Matteo Negri | Marco Turchi
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages. However, most of current evaluation practices adopt a word-level focus on a narrow set of occupational nouns under synthetic conditions. Such protocols overlook key features of grammatical gender languages, which are characterized by morphosyntactic chains of gender agreement, marked on a variety of lexical items and parts-of-speech (POS). To overcome this limitation, we enrich the natural, gender-sensitive MuST-SHE corpus (Bentivogli et al., 2020) with two new linguistic annotation layers (POS and agreement chains), and explore to what extent different lexical categories and agreement phenomena are impacted by gender skews. Focusing on speech translation, we conduct a multifaceted evaluation on three language directions (English-French/Italian/Spanish), with models trained on varying amounts of data and different word segmentation techniques. By shedding light on model behaviours, gender bias, and its detection at several levels of granularity, our findings emphasize the value of dedicated analyses beyond aggregated overall results.

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On the Dynamics of Gender Learning in Speech Translation
Beatrice Savoldi | Marco Gaido | Luisa Bentivogli | Matteo Negri | Marco Turchi
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics.

2021

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Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?
Luisa Bentivogli | Mauro Cettolo | Marco Gaido | Alina Karakanta | Alberto Martinelli | Matteo Negri | Marco Turchi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.

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How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation
Marco Gaido | Beatrice Savoldi | Luisa Bentivogli | Matteo Negri | Marco Turchi
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Gender Bias in Machine Translation
Beatrice Savoldi | Marco Gaido | Luisa Bentivogli | Matteo Negri | Marco Turchi
Transactions of the Association for Computational Linguistics, Volume 9

AbstractMachine translation (MT) technology has facilitated our daily tasks by providing accessible shortcuts for gathering, processing, and communicating information. However, it can suffer from biases that harm users and society at large. As a relatively new field of inquiry, studies of gender bias in MT still lack cohesion. This advocates for a unified framework to ease future research. To this end, we: i) critically review current conceptualizations of bias in light of theoretical insights from related disciplines, ii) summarize previous analyses aimed at assessing gender bias in MT, iii) discuss the mitigating strategies proposed so far, and iv) point toward potential directions for future work.

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Is “moby dick” a Whale or a Bird? Named Entities and Terminology in Speech Translation
Marco Gaido | Susana Rodríguez | Matteo Negri | Luisa Bentivogli | Marco Turchi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Automatic translation systems are known to struggle with rare words. Among these, named entities (NEs) and domain-specific terms are crucial, since errors in their translation can lead to severe meaning distortions. Despite their importance, previous speech translation (ST) studies have neglected them, also due to the dearth of publicly available resources tailored to their specific evaluation. To fill this gap, we i) present the first systematic analysis of the behavior of state-of-the-art ST systems in translating NEs and terminology, and ii) release NEuRoparl-ST, a novel benchmark built from European Parliament speeches annotated with NEs and terminology. Our experiments on the three language directions covered by our benchmark (en→es/fr/it) show that ST systems correctly translate 75–80% of terms and 65–70% of NEs, with very low performance (37–40%) on person names.

2020

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Breeding Gender-aware Direct Speech Translation Systems
Marco Gaido | Beatrice Savoldi | Luisa Bentivogli | Matteo Negri | Marco Turchi
Proceedings of the 28th International Conference on Computational Linguistics

In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more robust direct solutions. In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e.g.speaker’s vocal characteristics) that is otherwise lost in the cascade framework. Although such ability proved to be useful for gender translation, direct ST is nonetheless affected by gender bias just like its cascade counterpart, as well as machine translation and numerous other natural language processing applications. Moreover, direct ST systems that exclusively rely on vocal biometric features as a gender cue can be unsuitable or even potentially problematic for certain users. Going beyond speech signals, in this paper we compare different approaches to inform direct ST models about the speaker’s gender and test their ability to handle gender translation from English into Italian and French. To this aim, we manually annotated large datasets with speak-ers’ gender information and used them for experiments reflecting different possible real-world scenarios. Our results show that gender-aware direct ST solutions can significantly outperform strong – but gender-unaware – direct ST models. In particular, the translation of gender-marked words can increase up to 30 points in accuracy while preserving overall translation quality.

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Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
Luisa Bentivogli | Beatrice Savoldi | Matteo Negri | Mattia A. Di Gangi | Roldano Cattoni | Marco Turchi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines. This difficulty is also due to the fact that the training data on which models are built typically reflect the asymmetries of natural languages, gender bias included. Exclusively fed with textual data, machine translation is intrinsically constrained by the fact that the input sentence does not always contain clues about the gender identity of the referred human entities. But what happens with speech translation, where the input is an audio signal? Can audio provide additional information to reduce gender bias? We present the first thorough investigation of gender bias in speech translation, contributing with: i) the release of a benchmark useful for future studies, and ii) the comparison of different technologies (cascade and end-to-end) on two language directions (English-Italian/French).

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CEF Data Marketplace: Powering a Long-term Supply of Language Data
Amir Kamran | Dace Dzeguze | Jaap van der Meer | Milica Panic | Alessandro Cattelan | Daniele Patrioli | Luisa Bentivogli | Marco Turchi
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

We describe the CEF Data Marketplace project, which focuses on the development of a trading platform of translation data for language professionals: translators, machine translation (MT) developers, language service providers (LSPs), translation buyers and government bodies. The CEF Data Marketplace platform will be designed and built to manage and trade data for all languages and domains. This project will open a continuous and longterm supply of language data for MT and other machine learning applications.

2019

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MuST-C: a Multilingual Speech Translation Corpus
Mattia A. Di Gangi | Roldano Cattoni | Luisa Bentivogli | Matteo Negri | Marco Turchi
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)

Current research on spoken language translation (SLT) has to confront with the scarcity of sizeable and publicly available training corpora. This problem hinders the adoption of neural end-to-end approaches, which represent the state of the art in the two parent tasks of SLT: automatic speech recognition and machine translation. To fill this gap, we created MuST-C, a multilingual speech translation corpus whose size and quality will facilitate the training of end-to-end systems for SLT from English into 8 languages. For each target language, MuST-C comprises at least 385 hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual transcriptions and translations. Together with a description of the corpus creation methodology (scalable to add new data and cover new languages), we provide an empirical verification of its quality and SLT results computed with a state-of-the-art approach on each language direction.

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Machine Translation for Machines: the Sentiment Classification Use Case
Amirhossein Tebbifakhr | Luisa Bentivogli | Matteo Negri | Marco Turchi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency (“human-oriented” quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a “machine-oriented” criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with “machine-oriented” translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.

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MAGMATic: A Multi-domain Academic Gold Standard with Manual Annotation of Terminology for Machine Translation Evaluation
Randy Scansani | Luisa Bentivogli | Silvia Bernardini | Adriano Ferraresi
Proceedings of Machine Translation Summit XVII: Research Track

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Do translator trainees trust machine translation? An experiment on post-editing and revision
Randy Scansani | Silvia Bernardini | Adriano Ferraresi | Luisa Bentivogli
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks

2018

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Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment
Luisa Bentivogli | Mauro Cettolo | Marcello Federico | Christian Federmann
Proceedings of the 15th International Conference on Spoken Language Translation

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA). To this purpose, we exploit a publicly available large dataset containing both types of evaluations. We first focus on PE and investigate how sensitive TER-based evaluation is to the type and number of references used. Then, we carry out a comparative analysis of PE and DA to investigate the extent to which the evaluation results obtained by methodologies addressing different human perspectives are similar. This comparison sheds light not only on PE but also on the so-called reference bias related to monolingual DA. Also, we analyze if and how the two methodologies can complement each other’s weaknesses.

2017

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Overview of the IWSLT 2017 Evaluation Campaign
Mauro Cettolo | Marcello Federico | Luisa Bentivogli | Jan Niehues | Sebastian Stüker | Katsuhito Sudoh | Koichiro Yoshino | Christian Federmann
Proceedings of the 14th International Conference on Spoken Language Translation

The IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur in human-human dialogue turns. And, finally, the Lecture task, which offers the challenge of automatically transcribing and translating real-life university lectures. Following the tradition of these reports, we will described all tasks in detail and present the results of all runs submitted by their participants.

2016

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Neural versus Phrase-Based Machine Translation Quality: a Case Study
Luisa Bentivogli | Arianna Bisazza | Mauro Cettolo | Marcello Federico
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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The IWSLT 2016 Evaluation Campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Rolando Cattoni | Marcello Federico
Proceedings of the 13th International Conference on Spoken Language Translation

The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations. While the first task extends previously offered tasks with talks from a different source, the second task is completely new. For both tasks, three tracks were organised: automatic speech recognition (ASR), spoken language translation (SLT), and machine translation (MT). Main translation directions that were offered are English to/from German and English to French. Additionally, the MT track included English to/from Arabic and Czech, as well as French to English. We received this year run submissions from 11 research labs. All runs were evaluated with objective metrics, while submissions for two of the MT talk tasks were also evaluated with human post-editing. Results of the human evaluation show improvements over the best submissions of last year.

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WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
Luisa Bentivogli | Mauro Cettolo | M. Amin Farajian | Marcello Federico
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3% of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems’ performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.

2015

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The IWSLT 2015 Evaluation Campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Roldano Cattoni | Marcello Federico
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

2014

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Report on the 11th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, five speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and five text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the official tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the official text translation tracks were also evaluated with human post-editing.

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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli | Stefano Menini | Marco Baroni | Luisa Bentivogli | Raffaella Bernardi | Roberto Zamparelli
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.

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Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models
Marcello Federico | Matteo Negri | Luisa Bentivogli | Marco Turchi
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
Marco Marelli | Luisa Bentivogli | Marco Baroni | Raffaella Bernardi | Stefano Menini | Roberto Zamparelli
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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MT-EQuAl: a Toolkit for Human Assessment of Machine Translation Output
Christian Girardi | Luisa Bentivogli | Mohammad Amin Farajian | Marcello Federico
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

2013

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Report on the 10th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the tenth evaluation campaign organized by the IWSLT workshop. The 2013 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included two automatic speech recognition tracks, on English and German, three speech translation tracks, from English to French, English to German, and German to English, and three text translation track, also from English to French, English to German, and German to English. In addition to the official tracks, speech and text translation optional tracks were offered involving 12 other languages: Arabic, Spanish, Portuguese (B), Italian, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 18 teams participated in the evaluation for a total of 217 primary runs submitted. All runs were evaluated with objective metrics on a current test set and two progress test sets, in order to compare the progresses against systems of the previous years. In addition, submissions of one of the official machine translation tracks were also evaluated with human post-editing.

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Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content Synchronization
Matteo Negri | Alessandro Marchetti | Yashar Mehdad | Luisa Bentivogli | Danilo Giampiccolo
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
Myroslava Dzikovska | Rodney Nielsen | Chris Brew | Claudia Leacock | Danilo Giampiccolo | Luisa Bentivogli | Peter Clark | Ido Dagan | Hoa Trang Dang
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

2012

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Semeval-2012 Task 8: Cross-lingual Textual Entailment for Content Synchronization
Matteo Negri | Alessandro Marchetti | Yashar Mehdad | Luisa Bentivogli | Danilo Giampiccolo
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Crowd-based MT Evaluation for non-English Target Languages
Michael Paul | Eiichiro Sumita | Luisa Bentivogli | Marcello Federico
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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Chinese Whispers: Cooperative Paraphrase Acquisition
Matteo Negri | Yashar Mehdad | Alessandro Marchetti | Danilo Giampiccolo | Luisa Bentivogli
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a framework for the acquisition of sentential paraphrases based on crowdsourcing. The proposed method maximizes the lexical divergence between an original sentence s and its valid paraphrases by running a sequence of paraphrasing jobs carried out by a crowd of non-expert workers. Instead of collecting direct paraphrases of s, at each step of the sequence workers manipulate semantically equivalent reformulations produced in the previous round. We applied this method to paraphrase English sentences extracted from Wikipedia. Our results show that, keeping at each round n the most promising paraphrases (i.e. the more lexically dissimilar from those acquired at round n-1), the monotonic increase of divergence allows to collect good-quality paraphrases in a cost-effective manner.

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The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation
Marcello Federico | Sebastian Stüker | Luisa Bentivogli | Michael Paul | Mauro Cettolo | Teresa Herrmann | Jan Niehues | Giovanni Moretti
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series. That IWSLT 2011 evaluation focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. Unlike in previous years, all data supplied for the evaluation has been publicly released on the workshop website, and is at the disposal of researchers interested in working on our benchmarks and in comparing their results with those published at the workshop. This paper provides an overview of the IWSLT 2011 evaluation campaign, and describes the data supplied, the evaluation infrastructure made available to participants, and the subjective evaluation carried out.

2011

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Overview of the IWSLT 2011 evaluation campaign
Marcello Federico | Luisa Bentivogli | Michael Paul | Sebastian Stüker
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

We report here on the eighth Evaluation Campaign organized by the IWSLT workshop. This year, the IWSLT evaluation focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. Unlike previous years, all data supplied for the evaluation has been publicly released on the workshop website, and is at the disposal of researchers interested in working on our benchmarks and in comparing their results with those published at the workshop. This paper provides an overview of the IWSLT 2011 Evaluation Campaign, which includes: descriptions of the supplied data and evaluation specifications of each track, the list of participants specifying their submitted runs, a detailed description of the subjective evaluation carried out, the main findings of each exercise drawn from the results and the system descriptions prepared by the participants, and, finally, several detailed tables reporting all the evaluation results.

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Getting Expert Quality from the Crowd for Machine Translation Evaluation
Luisa Bentivogli | Marcello Federico | Giovanni Moretti | Michael Paul
Proceedings of Machine Translation Summit XIII: Papers

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Divide and Conquer: Crowdsourcing the Creation of Cross-Lingual Textual Entailment Corpora
Matteo Negri | Luisa Bentivogli | Yashar Mehdad | Danilo Giampiccolo | Alessandro Marchetti
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Building Textual Entailment Specialized Data Sets: a Methodology for Isolating Linguistic Phenomena Relevant to Inference
Luisa Bentivogli | Elena Cabrio | Ido Dagan | Danilo Giampiccolo | Medea Lo Leggio | Bernardo Magnini
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper proposes a methodology for the creation of specialized data sets for Textual Entailment, made of monothematic Text-Hypothesis pairs (i.e. pairs in which only one linguistic phenomenon relevant to the entailment relation is highlighted and isolated). The expected benefits derive from the intuition that investigating the linguistic phenomena separately, i.e. decomposing the complexity of the TE problem, would yield an improvement in the development of specific strategies to cope with them. The annotation procedure assumes that humans have knowledge about the linguistic phenomena relevant to inference, and a classification of such phenomena both into fine grained and macro categories is suggested. We experimented with the proposed methodology over a sample of pairs taken from the RTE-5 data set, and investigated critical issues arising when entailment, contradiction or unknown pairs are considered. The result is a new resource, which can be profitably used both to advance the comprehension of the linguistic phenomena relevant to entailment judgments and to make a first step towards the creation of large-scale specialized data sets.

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A Resource for Investigating the Impact of Anaphora and Coreference on Inference.
Azad Abad | Luisa Bentivogli | Ido Dagan | Danilo Giampiccolo | Shachar Mirkin | Emanuele Pianta | Asher Stern
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Discourse phenomena play a major role in text processing tasks. However, so far relatively little study has been devoted to the relevance of discourse phenomena for inference. Therefore, an experimental study was carried out to assess the relevance of anaphora and coreference for Textual Entailment (TE), a prominent inference framework. First, the annotation of anaphoric and coreferential links in the RTE-5 Search data set was performed according to a specifically designed annotation scheme. As a result, a new data set was created where all anaphora and coreference instances in the entailing sentences which are relevant to the entailment judgment are solved and annotated.. A by-product of the annotation is a new “augmented” data set, where all the referring expressions which need to be resolved in the entailing sentences are replaced by explicit expressions. Starting from the final output of the annotation, the actual impact of discourse phenomena on inference engines was investigated, identifying the kind of operations that the systems need to apply to address discourse phenomena and trying to find direct mappings between these operation and annotation types.

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Extending English ACE 2005 Corpus Annotation with Ground-truth Links to Wikipedia
Luisa Bentivogli | Pamela Forner | Claudio Giuliano | Alessandro Marchetti | Emanuele Pianta | Kateryna Tymoshenko
Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources

2006

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Representing and Accessing Multilevel Linguistic Annotation using the MEANING Format
Emanuele Pianta | Luisa Bentivogli | Christian Girardi | Bernardo Magnini
Proceedings of the 5th Workshop on NLP and XML (NLPXML-2006): Multi-Dimensional Markup in Natural Language Processing

2004

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Revising the Wordnet Domains Hierarchy: semantics, coverage and balancing
Luisa Bentivogli | Pamela Forner | Bernardo Magnini | Emanuele Pianta
Proceedings of the Workshop on Multilingual Linguistic Resources

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Evaluating Cross-Language Annotation Transfer in the MultiSemCor Corpus
Luisa Bentivogli | Pamela Forner | Emanuele Pianta
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Knowledge Intensive Word Alignment with KNOWA
Emanuele Pianta | Luisa Bentivogli
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Beyond Lexical Units: Enriching WordNets with Phrasets
Luisa Bentivogli | Emanuele Pianta
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Opportunistic Semantic Tagging
Luisa Bentivogli | Emanuele Pianta
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2000

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Coping with Lexical Gaps when Building Aligned Multilingual Wordnets
Luisa Bentivogli | Emanuele Pianta | Fabio Pianesi
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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