Raffaella Bernardi

Also published as: R. Bernardi


2021

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Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy
Alberto Testoni | Raffaella Bernardi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Generating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and sound unnatural to humans. Inspired by the cognitive literature on information search and cross-situational word learning, we design Confirm-it, a model based on a beam search re-ranking algorithm that guides an effective goal-oriented strategy by asking questions that confirm the model’s conjecture about the referent. We take the GuessWhat?! game as a case-study. We show that dialogues generated by Confirm-it are more natural and effective than beam search decoding without re-ranking.

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The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues
Alberto Testoni | Raffaella Bernardi
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

When training a model on referential dialogue guessing games, the best model is usually chosen based on its task success. We show that in the popular end-to-end approach, this choice prevents the model from learning to generate linguistically richer dialogues, since the acquisition of language proficiency takes longer than learning the guessing task. By comparing models playing different games (GuessWhat, GuessWhich, and Mutual Friends), we show that this discrepancy is model- and task-agnostic. We investigate whether and when better language quality could lead to higher task success. We show that in GuessWhat, models could increase their accuracy if they learn to ground, encode, and decode also words that do not occur frequently in the training set.

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I’ve Seen Things You People Wouldn’t Believe”: Hallucinating Entities in GuessWhat?!
Alberto Testoni | Raffaella Bernardi
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

Natural language generation systems have witnessed important progress in the last years, but they are shown to generate tokens that are unrelated to the source input. This problem affects computational models in many NLP tasks, and it is particularly unpleasant in multimodal systems. In this work, we assess the rate of object hallucination in multimodal conversational agents playing the GuessWhat?! referential game. Better visual processing has been shown to mitigate this issue in image captioning; hence, we adapt to the GuessWhat?! task the best visual processing models at disposal, and propose two new models to play the Questioner agent. We show that the new models generate few hallucinations compared to other renowned models available in the literature. Moreover, their hallucinations are less severe (affect task-accuracy less) and are more human-like. We also analyse where hallucinations tend to occur more often through the dialogue: hallucinations are less frequent in earlier turns, cause a cascade hallucination effect, and are often preceded by negative answers, which have been shown to be harder to ground.

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Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History
Tianai Dong | Alberto Testoni | Luciana Benotti | Raffaella Bernardi
Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics

In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues. We say that a question is history-dependent if it requires (parts of) its dialogue history to be interpreted. We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies. We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer. We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g. color, number). We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger. We implement a simple baseline architecture based on a SOTA multimodal encoder. Our results reveal that there is much room for improvement for answering history-dependent questions.

2020

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They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies
Alberto Testoni | Claudio Greco | Tobias Bianchi | Mauricio Mazuecos | Agata Marcante | Luciana Benotti | Raffaella Bernardi
Proceedings of the Third International Workshop on Spatial Language Understanding

In this paper, we study the grounding skills required to answer spatial questions asked by humans while playing the GuessWhat?! game. We propose a classification for spatial questions dividing them into absolute, relational, and group questions. We build a new answerer model based on the LXMERT multimodal transformer and we compare a baseline with and without visual features of the scene. We are interested in studying how the attention mechanisms of LXMERT are used to answer spatial questions since they require putting attention on more than one region simultaneously and spotting the relation holding among them. We show that our proposed model outperforms the baseline by a large extent (9.70% on spatial questions and 6.27% overall). By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.

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Effective questions in referential visual dialogue
Mauricio Mazuecos | Alberto Testoni | Raffaella Bernardi | Luciana Benotti
Proceedings of the The Fourth Widening Natural Language Processing Workshop

An interesting challenge for situated dialogue systems is referential visual dialog: by asking questions, the system has to identify the referent to which the user refers to. Task success is the standard metric used to evaluate these systems. However, it does not consider how effective each question is, that is how much each question contributes to the goal. We propose a new metric, that measures question effectiveness. As a preliminary study, we report the new metric for state of the art publicly available models on GuessWhat?!. Surprisingly, successful dialogues do not have a higher percentage of effective questions than failed dialogues. This suggests that a system with high task success is not necessarily one that generates good questions.

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On the role of effective and referring questions in GuessWhat?!
Mauricio Mazuecos | Alberto Testoni | Raffaella Bernardi | Luciana Benotti
Proceedings of the First Workshop on Advances in Language and Vision Research

Task success is the standard metric used to evaluate referential visual dialogue systems. In this paper we propose two new metrics that evaluate how each question contributes to the goal. First, we measure how effective each question is by evaluating whether the question discards objects that are not the referent. Second, we define referring questions as those that univocally identify one object in the image. We report the new metrics for human dialogues and for state of the art publicly available models on GuessWhat?!. Regarding our first metric, we find that successful dialogues do not have a higher percentage of effective questions for most models. With respect to the second metric, humans make questions at the end of the dialogue that are referring, confirming their guess before guessing. Human dialogues that use this strategy have a higher task success but models do not seem to learn it.

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Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision
Sandro Pezzelle | Claudio Greco | Greta Gandolfi | Eleonora Gualdoni | Raffaella Bernardi
Findings of the Association for Computational Linguistics: EMNLP 2020

This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities. Recently, impressive progress has been made to develop universal multimodal encoders suitable for virtually any language and vision tasks. However, current approaches often require them to combine redundant information provided by language and vision. Inspired by real-life communicative contexts, we propose a novel task where either modality is necessary but not sufficient to make a correct prediction. To do so, we first build a dataset of images and corresponding sentences provided by human participants. Second, we evaluate state-of-the-art models and compare their performance against human speakers. We show that, while the task is relatively easy for humans, best-performing models struggle to achieve similar results.

2019

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Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
Claudio Greco | Barbara Plank | Raquel Fernández | Raffaella Bernardi
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA). Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions). We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models. Our results show that dramatic forgetting is at play and that task difficulty and order matter. Two well-known current continual learning methods mitigate the problem only to a limiting degree.

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Evaluating the Representational Hub of Language and Vision Models
Ravi Shekhar | Ece Takmaz | Raquel Fernández | Raffaella Bernardi
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the “Hub and Spoke” architecture proposed in cognitive science to represent how the brain processes and combines multi-sensory inputs. In particular, the Hub is implemented as a neural network encoder. We investigate the effect on this encoder of various vision-and-language tasks proposed in the literature: visual question answering, visual reference resolution, and visually grounded dialogue. To measure the quality of the representations learned by the encoder, we use two kinds of analyses. First, we evaluate the encoder pre-trained on the different vision-and-language tasks on an existing “diagnostic task” designed to assess multimodal semantic understanding. Second, we carry out a battery of analyses aimed at studying how the encoder merges and exploits the two modalities.

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Proceedings of the Second Workshop on Shortcomings in Vision and Language
Raffaella Bernardi | Raquel Fernandez | Spandana Gella | Kushal Kafle | Christopher Kanan | Stefan Lee | Moin Nabi
Proceedings of the Second Workshop on Shortcomings in Vision and Language

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Quantifiers in a Multimodal World: Hallucinating Vision with Language and Sound
Alberto Testoni | Sandro Pezzelle | Raffaella Bernardi
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Inspired by the literature on multisensory integration, we develop a computational model to ground quantifiers in perception. The model learns to pick, out of nine quantifiers (‘few’, ‘many’, ‘all’, etc.), the one that is more likely to describe the percent of animals in a visual-auditory input containing both animals and artifacts. We show that relying on concurrent sensory inputs increases model performance on the quantification task. Moreover, we evaluate the model in a situation in which only the auditory modality is given, while the visual one is ‘hallucinanted’ either from the auditory input itself or from a linguistic caption describing the quantity of entities in the auditory input. This way, the model exploits prior associations between modalities. We show that the model profits from the prior knowledge and outperforms the auditory-only setting.

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Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat
Ravi Shekhar | Aashish Venkatesh | Tim Baumgärtner | Elia Bruni | Barbara Plank | Raffaella Bernardi | Raquel Fernández
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)

We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions. Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning. We further enrich our model via a cooperative learning regime. We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system. We compare our approach to an alternative system which extends the baseline with reinforcement learning. Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels. This points at the importance of analyzing the linguistic output of competing systems beyond numeric comparison solely based on task success.

2018

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Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Sandro Pezzelle | Shane Steinert-Threlkeld | Raffaella Bernardi | Jakub Szymanik
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We study the role of linguistic context in predicting quantifiers (‘few’, ‘all’). We collect crowdsourced data from human participants and test various models in a local (single-sentence) and a global context (multi-sentence) condition. Models significantly out-perform humans in the former setting and are only slightly better in the latter. While human performance improves with more linguistic context (especially on proportional quantifiers), model performance suffers. Models are very effective in exploiting lexical and morpho-syntactic patterns; humans are better at genuinely understanding the meaning of the (global) context.

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Ask No More: Deciding when to guess in referential visual dialogue
Ravi Shekhar | Tim Baumgärtner | Aashish Venkatesh | Elia Bruni | Raffaella Bernardi | Raquel Fernandez
Proceedings of the 27th International Conference on Computational Linguistics

Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.

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Grounded Textual Entailment
Hoa Trong Vu | Claudio Greco | Aliia Erofeeva | Somayeh Jafaritazehjan | Guido Linders | Marc Tanti | Alberto Testoni | Raffaella Bernardi | Albert Gatt
Proceedings of the 27th International Conference on Computational Linguistics

Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise P entails a hypothesis H iff in all worlds where P is true, H is also true. Statistical models view this relationship probabilistically, addressing it in terms of whether a human would likely infer H from P. In this paper, we wish to bridge these two perspectives, by arguing for a visually-grounded version of the Textual Entailment task. Specifically, we ask whether models can perform better if, in addition to P and H, there is also an image (corresponding to the relevant “world” or “situation”). We use a multimodal version of the SNLI dataset (Bowman et al., 2015) and we compare “blind” and visually-augmented models of textual entailment. We show that visual information is beneficial, but we also conduct an in-depth error analysis that reveals that current multimodal models are not performing “grounding” in an optimal fashion.

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Comparatives, Quantifiers, Proportions: a Multi-Task Model for the Learning of Quantities from Vision
Sandro Pezzelle | Ionut-Teodor Sorodoc | Raffaella Bernardi
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes. We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation. Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects. Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.

2017

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FOIL it! Find One mismatch between Image and Language caption
Ravi Shekhar | Sandro Pezzelle | Yauhen Klimovich | Aurélie Herbelot | Moin Nabi | Enver Sangineto | Raffaella Bernardi
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MS-COCO dataset, FOIL-COCO, which associates images with both correct and ‘foil’ captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake (‘foil word’). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.

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Vision and Language Integration: Moving beyond Objects
Ravi Shekhar | Sandro Pezzelle | Aurélie Herbelot | Moin Nabi | Enver Sangineto | Raffaella Bernardi
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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Can You See the (Linguistic) Difference? Exploring Mass/Count Distinction in Vision
David Addison Smith | Sandro Pezzelle | Francesca Franzon | Chiara Zanini | Raffaella Bernardi
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

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Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Sandro Pezzelle | Marco Marelli | Raffaella Bernardi
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two processes underlie fairly different cognitive and neural mechanisms. Inspired by this evidence, the present study proposes two models for learning the objective meaning of cardinals and quantifiers from visual scenes containing multiple objects. We show that a model capitalizing on a ‘fuzzy’ measure of similarity is effective for learning quantifiers, whereas the learning of exact cardinals is better accomplished when information about number is provided.

2016

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Building a Bagpipe with a Bag and a Pipe: Exploring Conceptual Combination in Vision
Sandro Pezzelle | Ravi Shekhar | Raffaella Bernardi
Proceedings of the 5th Workshop on Vision and Language

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“Look, some Green Circles!”: Learning to Quantify from Images
Ionut Sorodoc | Angeliki Lazaridou | Gemma Boleda | Aurélie Herbelot | Sandro Pezzelle | Raffaella Bernardi
Proceedings of the 5th Workshop on Vision and Language

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Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics
Claire Gardent | Raffaella Bernardi | Ivan Titov
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics
Germán Kruszewski | Denis Paperno | Raffaella Bernardi | Marco Baroni
Computational Linguistics, Volume 42, Issue 4 - December 2016

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The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno | Germán Kruszewski | Angeliki Lazaridou | Ngoc Quan Pham | Raffaella Bernardi | Sandro Pezzelle | Marco Baroni | Gemma Boleda | Raquel Fernández
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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Distributional Semantics in Use
Raffaella Bernardi | Gemma Boleda | Raquel Fernández | Denis Paperno
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

2014

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Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)
Alexandre Allauzen | Raffaella Bernardi | Edward Grefenstette | Hugo Larochelle | Christopher Manning | Scott Wen-tau Yih
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)

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TUHOI: Trento Universal Human Object Interaction Dataset
Dieu-Thu Le | Jasper Uijlings | Raffaella Bernardi
Proceedings of the Third Workshop on Vision and Language

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Coloring Objects: Adjective-Noun Visual Semantic Compositionality
Dat Tien Nguyen | Angeliki Lazaridou | Raffaella Bernardi
Proceedings of the Third Workshop on Vision and Language

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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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Frege in Space: A Program for Composition Distributional Semantics
Marco Baroni | Raffaella Bernardi | Roberto Zamparelli
Linguistic Issues in Language Technology, Volume 9, 2014 - Perspectives on Semantic Representations for Textual Inference

The lexicon of any natural language encodes a huge number of distinct word meanings. Just to understand this article, you will need to know what thousands of words mean. The space of possible sentential meanings is infinite: In this article alone, you will encounter many sentences that express ideas you have never heard before, we hope. Statistical semantics has addressed the issue of the vastness of word meaning by proposing methods to harvest meaning automatically from large collections of text (corpora). Formal semantics in the Fregean tradition has developed methods to account for the infinity of sentential meaning based on the crucial insight of compositionality, the idea that meaning of sentences is built incrementally by combining the meanings of their constituents. This article sketches a new approach to semantics that brings together ideas from statistical and formal semantics to account, in parallel, for the richness of lexical meaning and the combinatorial power of sentential semantics. We adopt, in particular, the idea that word meaning can be approximated by the patterns of co-occurrence of words in corpora from statistical semantics, and the idea that compositionality can be captured in terms of a syntax-driven calculus of function application from formal semantics.

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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.

2013

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Exploiting Language Models for Visual Recognition
Dieu-Thu Le | Jasper Uijlings | Raffaella Bernardi
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A relatedness benchmark to test the role of determiners in compositional distributional semantics
Raffaella Bernardi | Georgiana Dinu | Marco Marelli | Marco Baroni
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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CCG Categories for Distributional Semantic Models
Paramita Mirza | Raffaella Bernardi
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Sentence paraphrase detection: When determiners and word order make the difference
Nghia Pham | Raffaella Bernardi | Yao Zhong Zhang | Marco Baroni
Proceedings of the IWCS 2013 Workshop Towards a Formal Distributional Semantics

2012

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Entailment above the word level in distributional semantics
Marco Baroni | Raffaella Bernardi | Ngoc-Quynh Do | Chung-chieh Shan
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Query classification using topic models and support vector machine
Dieu-Thu Le | Raffaella Bernardi
Proceedings of ACL 2012 Student Research Workshop

2011

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Query classification via Topic Models for an art image archive
Dieu-Thu Le | Raffaella Bernardi | Ed Vald
Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage

2010

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Towards an Empirically Motivated Typology of Follow-Up Questions: The Role of Dialogue Context
Manuel Kirschner | Raffaella Bernardi
Proceedings of the SIGDIAL 2010 Conference

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Context Fusion: The Role of Discourse Structure and Centering Theory
Raffaella Bernardi | Manuel Kirschner | Zorana Ratkovic
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Questions are not asked in isolation. Their context, viz. the preceding interactions, might be of help to understand them and retrieve the correct answer. Previous research in Interactive Question Answering showed that context fusion has a big potential to improve the performance of answer retrieval. In this paper, we study how much context, and what elements of it, should be considered to answer Follow-Up Questions (FU Qs). Following previous research, we exploit Logistic Regression Models to learn aspects of dialogue structure relevant to answering FU Qs. We enrich existing models based on shallow features with deep features, relying on the theory of discourse structure of (Chai and Jin, 2004), and on Centering Theory, respectively. Using models trained on realistic IQA data, we show which of the various theoretically motivated features hold up against empirical evidence. We also show that, while these deep features do not outperform the shallow ones on their own, an IQA system's answer correctness increases if the shallow and deep features are combined.

2009

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Exploring Topic Continuation Follow-up Questions using Machine Learning
Manuel Kirschner | Raffaella Bernardi
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

2008

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Context Modelling for IQA: the Role of Tasks and Entities
Raffaella Bernardi | Manuel Kirschner
Coling 2008: Proceedings of the workshop on Knowledge and Reasoning for Answering Questions

2007

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An Empirical View on IQA Follow-up Questions
Manuel Kirschner | Raffaella Bernardi
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

2006

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Multilingual Search in Libraries. The case-study of the Free University of Bozen-Bolzano
R. Bernardi | D. Calvanese | L. Dini | V. Di Tomaso | E. Frasnelli | U. Kugler | B. Plank
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper presents an on-going project aiming at enhancing the OPAC (Online Public Access Catalog) search system of the Library of the Free University of Bozen-Bolzano with multilingual access. The Multilingual search system (MUSIL), we have developed, integrates advanced linguistic technologies in a user friendly interface and bridges the gap between the world of free text search and the world of conceptual librarian search. In this paper we present the architecture of the system, its interface and preliminary evaluations of the precision of the search results.

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POS tagset design for Italian
Raffaella Bernardi | Andrea Bolognesi | Corrado Seidenari | Fabio Tamburini
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We aim to automatically induce a PoS tagset for Italian by analysing the distributional behaviour of Italian words. To this end, we propose an algorithm that (a) extracts information from loosely labelled dependency structures that encode only basic and broadly accepted syntactic relations, namely Head/Dependent and the distinction of dependents into Argument vs. Adjunct, and (b) derives a possible set of word classes. The paper reports on some preliminary experiments carried out using the induced tagset in conjunction with state-of-the-art PoS taggers. The method proposed to design a proper tagset exploits little, if any, language-specific knowledge: hence it is in principle applicable to any language.

2005

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Automatic Induction of a POS Tagset for Italian
Raffaella Bernardi | Andrea Bolognesi | Corrado Seidenari | Fabio Tamburini
Proceedings of the Australasian Language Technology Workshop 2005

2004

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Categorial Type Logic meets Dependency Grammar to annotate an Italian corpus
R. Bernardi | A. Bolognesi | F. Tamburini | M. Moortgat
Proceedings of the Workshop on Recent Advances in Dependency Grammar

2000

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Deriving polarity effects
Raffaella Bernardi
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)