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Enrique Alfonseca

Enrique Alfonseca

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    SKILL: Structured Knowledge Infusion for Large Language Models
    Fedor Moiseev
    Zhe Dong
    Martin Jaggi
    NAACL 2022 (2022) (to appear)
    Preview abstract Large language models (LLMs) have demonstrated human-level performance on vast spectrum of natural language tasks. However, whether they could efficiently memorize or learn from an abstract and structured corpus, like knowledge graph, is largely unexplored. In this work, we propose a method to infuse structure knowledge in LLM, by directly training T5 models on factual triples of knowledge graphs. By evaluating on closed-book QA tasks, we show that models pre-trained with our knowledge-infusing method outperform the T5 baselines, and performs competitively with the models pre-trained on natural language sentences that contain the same knowledge. The proposed method has an advantage that no alignment between the knowledge graph and text corpus is required to curate the training data. This make our method adaptable to industrial scale knowledge graph. View details
    Using Audio Transformations to Improve Comprehension in Voice Question Answering
    Johanne R. Trippas
    Hanna Silen
    Damiano Spina
    Crestani F. et al. (eds) Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019, Springer, Cham, pp. 164-170
    Preview abstract Many popular form factors of digital assistants—such as Amazon Echo, Apple Homepod, or Google Home—enable the user to hold a conversation with these systems based only on the speech modality. The lack of a screen presents unique challenges. To satisfy the information need of a user, the presentation of the answer needs to be optimized for such voice-only interactions. In this paper, we propose a task of evaluating the usefulness of audio transformations (i.e., prosodic modifications) for voice-only question answering. We introduce a crowdsourcing setup where we evaluate the quality of our proposed modifications along multiple dimensions corresponding to the informativeness, naturalness, and ability of the user to identify key parts of the answer. We offer a set of prosodic modifications that highlight potentially important parts of the answer using various acoustic cues. Our experiments show that some of these modifications lead to better comprehension at the expense of only slightly degraded naturalness of the audio. View details
    Preview abstract In this paper we study various flavors of variational autoencoders and address the methodological issues with the current neural text generation research and also close some gaps by answering a few natural questions to the studies already published. View details
    Preview abstract Users try to articulate their complex information needs during search sessions by reformulating their queries. In order to make this process more effective, search engines provide related queries to help users to specify the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion.In our model, we employ a query-aware attention mechanism to capture the structure of the session context. This enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that based on user query reformulation behavior, a large portion of terms of a query in a session is retained from the previously submitted queries in the same session and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. The results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion. View details
    Preview abstract A popular approach to sentence compression is to formulate the task as a constrained optimization problem and solve it with integer linear programming (ILP) tools. Unfortunately, dependence on ILP may make the compressor prohibitively slow, and thus approximation techniques have been proposed which are often complex and offer a moderate gain in speed. As an alternative solution, we introduce a novel compression algorithm which generates k-best compressions relying on local deletion decisions. Our algorithm is two orders of magnitude faster than a recent ILP-based method while producing better compressions. Moreover, an extensive evaluation demonstrates that the quality of compressions does not degrade much as we move from single best to top-five results. View details
    Sentence Compression by Deletion with LSTMs
    Lukasz Kaiser
    Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP'15)
    Preview abstract We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, corresponding to token deletion decisions. We demonstrate that even the most basic version of the system, which is given no syntactic information (no PoS or NE tags, or dependencies) or desired compression length, performs surprisingly well: around 30% of the compressions from a large test set could be regenerated. We compare the LSTM system with a competitive baseline which is trained on the same amount of data but is additionally provided with all kinds of linguistic features. In an experiment with human raters the LSTM-based model outperforms the baseline achieving 4.5 in readability and 3.8 in informativeness. View details
    Idest: Learning a Distributed Representation for Event Patterns
    Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL'15), pp. 1140-1149
    Preview abstract This paper describes IDEST, a new method for learning paraphrases of event patterns. It is based on a new neural network architecture that only relies on the weak supervision signal that comes from the news published on the same day and mention the same real-world entities. It can generalize across extractions from different dates to produce a robust paraphrase model for event patterns that can also capture meaningful representations for rare patterns. We compare it with two state-of-the-art systems and show that it can attain comparable quality when trained on a small dataset. Its generalization capabilities also allow it to leverage much more data, leading to substantial quality improvements. View details
    Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization
    Marco Cornolti
    Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL'14) (2014), pp. 892-901
    Preview abstract Abstractive text summarization of news requires a way of representing events, such as a collection of pattern clusters in which every cluster represents an event (e.g., marriage) and every pattern in the cluster is a way of expressing the event (e.g., X married Y, X and Y tied the knot). We compare three ways of extracting event patterns: heuristics-based, compression-based and memory-based. While the former has been used previously in multi-document abstraction, the latter two have never been used for this task. Compared with the first two techniques, the memory-based method allows for generating significantly more grammatical and informative sentences, at the cost of searching a vast space of hundreds of millions of parse trees of known grammatical utterances. To this end, we introduce a data structure and a search method that make it possible to efficiently extrapolate from every sentence the parse sub-trees that match against any of the stored utterances. View details
    WHAD: Wikipedia historical attributes data
    Guillermo Garrido
    Jean-Yves Delort
    Anselmo Peñas
    Language Resources and Evaluation (2013), pp. 28
    Preview abstract This paper describes the generation of temporally anchored infobox attribute data from the Wikipedia history of revisions. By mining (attribute, value) pairs from the revision history of the English Wikipedia we are able to collect a comprehensive knowledge base that contains data on how attributes change over time. When dealing with the Wikipedia edit history, vandalic and erroneous edits are a concern for data quality. We present a study of vandalism identification in Wikipedia edits that uses only features from the infoboxes, and show that we can obtain, on this dataset, an accuracy comparable to a state-of-the-art vandalism identification method that is based on the whole article. Finally, we discuss different characteristics of the extracted dataset, which we make available for further study. View details
    Preview abstract This paper presents HEADY: a novel, ab- stractive approach for headline generation from news collections. From a web-scale corpus of English news, we mine syntactic patterns that a Noisy-OR model generalizes into event descriptions. At inference time, we query the model with the patterns observed in an unseen news collection, identify the event that better captures the gist of the collection and retrieve the most appropriate pattern to generate a headline. HEADY improves over a state-of-the- art open-domain title abstraction method, bridging half of the gap that separates it from extractive methods using human-generated titles in manual evaluations, and performs comparably to human-generated headlines as evaluated with ROUGE. View details
    DualSum: A Topic-Model for Update Summarization
    Jean-Yves Delort
    Proceedings of EACL-2012, Brandschenkestrasse 110
    Preview
    Pattern Learning for Relation Extraction with Hierarchical Topic Models
    Jean-Yves Delort
    Guillermo Garrido
    Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL'12) (2012)
    Preview
    DiversiWeb 2011
    Elena Paslaru Bontas Simperl
    Devika P. Madalli
    Denny Vrandecic
    SIGIR Forum, vol. 45 (2011), pp. 49-53
    Preview
    DiversiWeb 2011: first international workshop on knowledge diversity on the web
    Elena Paslaru Bontas Simperl
    Devika P. Madalli
    Denny Vrandecic
    WWW (Companion Volume) (2011), pp. 319-320
    Preview
    Generalized Syntactic and Semantic Models of Query Reformulation
    Amac Herdagdelen
    Daniel Mahler
    Maria Holmqvist
    Keith Hall
    Stefan Riezler
    Proceedings of SIGIR-2010
    Preview
    Acquisition of Instance Attributes via Labeled and Related Instances
    Enrique Robledo-Arnuncio
    Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2010), Geneva, Switzerland, pp. 58-65
    Preview
    The Role of Query Sessions in Extracting Instance Attributes from Web Search Queries
    Enrique Robledo-Arnuncio
    Ricardo Martin-Brualla
    Keith Hall
    Proceedings of the 32nd European Conference on Information Retrieval (ECIR-2010), Milton Keynes, United Kingdom, pp. 62-74
    Preview
    A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches
    Eneko Agirre
    Keith Hall
    Jana Kravalova
    Aitor Soroa
    Proceedings of the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL-2009), Boulder, Colorado, pp. 19-27
    Preview
    Web-Derived Resources for Web Information Retrieval: From Conceptual Hierarchies to Attribute Hierarchies
    Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2009), Boston, Massachusetts, pp. 596-603
    Preview
    Gazpacho and summer rash: lexical relationships from temporal patterns of web search queries
    Keith Hall
    Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP) (2009)
    Preview
    German decompounding in a difficult corpus
    Slaven Bilac
    Stefan Pharies
    Proceedings of CICLING-2008, Lecture Notes in Computer Science, Springer, pp. 128-139
    Preview
    Reconocimiento de Entidades, Resolución de Correferencia y Extracción de Relaciones
    F. Verdejo (ed.), Acceso y viabilidad de la información multilingüe en la red: el rol de la semántica, Fundación Duques de Soria (2007)
    Preview
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