Diarmuid Ó Séaghdha


2020

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Conversational Semantic Parsing for Dialog State Tracking
Jianpeng Cheng | Devang Agrawal | Héctor Martínez Alonso | Shruti Bhargava | Joris Driesen | Federico Flego | Dain Kaplan | Dimitri Kartsaklis | Lin Li | Dhivya Piraviperumal | Jason D. Williams | Hong Yu | Diarmuid Ó Séaghdha | Anders Johannsen
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We consider a new perspective on dialog state tracking (DST), the task of estimating a user’s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to ~20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.

2017

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Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Ivan Vulić | Nikola Mrkšić | Roi Reichart | Diarmuid Ó Séaghdha | Steve Young | Anna Korhonen
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that ‘inexpensive’ is a rephrasing for ‘expensive’ or may not associate ‘acquire’ with ‘acquires’. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.

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Neural Belief Tracker: Data-Driven Dialogue State Tracking
Nikola Mrkšić | Diarmuid Ó Séaghdha | Tsung-Hsien Wen | Blaise Thomson | Steve Young
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user’s goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users’ language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.

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Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Nikola Mrkšić | Ivan Vulić | Diarmuid Ó Séaghdha | Ira Leviant | Roi Reichart | Milica Gašić | Anna Korhonen | Steve Young
Transactions of the Association for Computational Linguistics, Volume 5

We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the use of constraints from mono- and cross-lingual resources, yielding semantically specialized cross-lingual vector spaces. Our evaluation shows that the method can make use of existing cross-lingual lexicons to construct high-quality vector spaces for a plethora of different languages, facilitating semantic transfer from high- to lower-resource ones. The effectiveness of our approach is demonstrated with state-of-the-art results on semantic similarity datasets in six languages. We next show that Attract-Repel-specialized vectors boost performance in the downstream task of dialogue state tracking (DST) across multiple languages. Finally, we show that cross-lingual vector spaces produced by our algorithm facilitate the training of multilingual DST models, which brings further performance improvements.

2016

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Counter-fitting Word Vectors to Linguistic Constraints
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Lina M. Rojas-Barahona | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Learning Semantic Relations from Text
Preslav Nakov | Vivi Nastase | Diarmuid Ó Séaghdha | Stan Szpakowicz
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Every non-trivial text describes interactions and relations between people, institutions, activities, events and so on. What we know about the world consists in large part of such relations, and that knowledge contributes to the understanding of what texts refer to. Newly found relations can in turn become part of this knowledge that is stored for future use.To grasp a text’s semantic content, an automatic system must be able to recognize relations in texts and reason about them. This may be done by applying and updating previously acquired knowledge. We focus here in particular on semantic relations which describe the interactions among nouns and compact noun phrases, and we present such relations from both a theoretical and a practical perspective. The theoretical exploration sketches the historical path which has brought us to the contemporary view and interpretation of semantic relations. We discuss a wide range of relation inventories proposed by linguists and by language processing people. Such inventories vary by domain, granularity and suitability for downstream applications.On the practical side, we investigate the recognition and acquisition of relations from texts. In a look at supervised learning methods, we present available datasets, the variety of features which can describe relation instances, and learning algorithms found appropriate for the task. Next, we present weakly supervised and unsupervised learning methods of acquiring relations from large corpora with little or no previously annotated data. We show how enduring the bootstrapping algorithm based on seed examples or patterns has proved to be, and how it has been adapted to tackle Web-scale text collections. We also show a few machine learning techniques which can perform fast and reliable relation extraction by taking advantage of data redundancy and variability.

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Multi-domain Dialog State Tracking using Recurrent Neural Networks
Nikola Mrkšić | Diarmuid Ó Séaghdha | Blaise Thomson | Milica Gašić | Pei-Hao Su | David Vandyke | Tsung-Hsien Wen | Steve Young
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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Probabilistic Distributional Semantics with Latent Variable Models
Diarmuid Ó Séaghdha | Anna Korhonen
Computational Linguistics, Volume 40, Issue 3 - September 2014

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Unsupervised learning of rhetorical structure with un-topic models
Diarmuid Ó Séaghdha | Simone Teufel
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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CRAB 2.0: A text mining tool for supporting literature review in chemical cancer risk assessment
Yufan Guo | Diarmuid Ó Séaghdha | Ilona Silins | Lin Sun | Johan Högberg | Ulla Stenius | Anna Korhonen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

2013

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SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
Iris Hendrickx | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Stan Szpakowicz | Tony Veale
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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Modelling selectional preferences in a lexical hierarchy
Diarmuid Ó Séaghdha | Anna Korhonen
*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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Learning Syntactic Verb Frames using Graphical Models
Thomas Lippincott | Anna Korhonen | Diarmuid Ó Séaghdha
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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Probabilistic models of similarity in syntactic context
Diarmuid Ó Séaghdha | Anna Korhonen
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
Su Nam Kim | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Sebastian Padó | Stan Szpakowicz
Proceedings of the ACL 2011 Workshop on Relational Models of Semantics

2010

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Exploring variation across biomedical subdomains
Tom Lippincott | Diarmuid Ó Séaghdha | Lin Sun | Anna Korhonen
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Latent Variable Models of Selectional Preference
Diarmuid Ó Séaghdha
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals
Iris Hendrickx | Su Nam Kim | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Sebastian Padó | Marco Pennacchiotti | Lorenza Romano | Stan Szpakowicz
Proceedings of the 5th International Workshop on Semantic Evaluation

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SemEval-2 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions
Cristina Butnariu | Su Nam Kim | Preslav Nakov | Diarmuid Ó Séaghdha | Stan Szpakowicz | Tony Veale
Proceedings of the 5th International Workshop on Semantic Evaluation

2009

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Biomedical Event Extraction without Training Data
Andreas Vlachos | Paula Buttery | Diarmuid Ó Séaghdha | Ted Briscoe
Proceedings of the BioNLP 2009 Workshop Companion Volume for Shared Task

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SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
Iris Hendrickx | Su Nam Kim | Zornitsa Kozareva | Preslav Nakov | Diarmuid Ó Séaghdha | Sebastian Padó | Marco Pennacchiotti | Lorenza Romano | Stan Szpakowicz
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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SemEval-2010 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions
Cristina Butnariu | Su Nam Kim | Preslav Nakov | Diarmuid Ó Séaghdha | Stan Szpakowicz | Tony Veale
Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009)

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Using Lexical and Relational Similarity to Classify Semantic Relations
Diarmuid Ó Séaghdha | Ann Copestake
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Semantic Classification with WordNet Kernels
Diarmuid Ó Séaghdha
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Semantic Classification with Distributional Kernels
Diarmuid Ó Séaghdha | Ann Copestake
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Co-occurrence Contexts for Noun Compound Interpretation
Diarmuid Ó Séaghdha | Ann Copestake
Proceedings of the Workshop on A Broader Perspective on Multiword Expressions

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Annotating and Learning Compound Noun Semantics
Diarmuid Ó Séaghdha
Proceedings of the ACL 2007 Student Research Workshop