Richard Crouch


Hy-NLI: a Hybrid system for Natural Language Inference
Aikaterini-Lida Kalouli | Richard Crouch | Valeria de Paiva
Proceedings of the 28th International Conference on Computational Linguistics

Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena. Such phenomena, however, can be handled well by symbolic systems. Thus, we propose Hy-NLI, a hybrid system that learns to identify an NLI pair as linguistically challenging or not. Based on that, it uses its symbolic or deep learning component, respectively, to make the final inference decision. We show how linguistically less complex cases are best solved by robust state-of-the-art models, like BERT and XLNet, while hard linguistic phenomena are best handled by our implemented symbolic engine. Our thorough evaluation shows that our hybrid system achieves state-of-the-art performance across mainstream and adversarial datasets and opens the way for further research into the hybrid direction.

XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Aikaterini-Lida Kalouli | Rita Sevastjanova | Valeria de Paiva | Richard Crouch | Mennatallah El-Assady
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.


GKR: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations
Aikaterini-Lida Kalouli | Richard Crouch | Valeria de Paiva
Proceedings of the First International Workshop on Designing Meaning Representations

Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation. This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features. We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.

Composing Noun Phrase Vector Representations
Aikaterini-Lida Kalouli | Valeria de Paiva | Richard Crouch
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Vector representations of words have seen an increasing success over the past years in a variety of NLP tasks. While there seems to be a consensus about the usefulness of word embeddings and how to learn them, it is still unclear which representations can capture the meaning of phrases or even whole sentences. Recent work has shown that simple operations outperform more complex deep architectures. In this work, we propose two novel constraints for computing noun phrase vector representations. First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning. Second, the composition process of the two phrase vectors should apply suitable dimensions’ selection in a way that specific semantic features captured by the phrase’s meaning become more salient. Our proposed methods are compared to 11 other approaches, including popular baselines and a neural net architecture, and are evaluated across 6 tasks and 2 datasets. Our results show that these constraints lead to more expressive phrase representations and can be applied to other state-of-the-art methods to improve their performance.


GKR: the Graphical Knowledge Representation for semantic parsing
Aikaterini-Lida Kalouli | Richard Crouch
Proceedings of the Workshop on Computational Semantics beyond Events and Roles

This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85% in accurately representing sentences as semantic graphs.

Named Graphs for Semantic Representation
Richard Crouch | Aikaterini-Lida Kalouli
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction. Moving from graphs to named graphs leads to representations that stand some chance of having sufficient expressive power. Named ℱℒ0 graphs are of particular interest.


Local Textual Inference: Can it be Defined or Circumscribed?
Annie Zaenen | Lauri Karttunen | Richard Crouch
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment


Speed and Accuracy in Shallow and Deep Stochastic Parsing
Ron Kaplan | Stefan Riezler | Tracy H. King | John T. Maxwell III | Alex Vasserman | Richard Crouch
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004


Statistical Sentence Condensation using Ambiguity Packing and Stochastic Disambiguation Methods for Lexical-Functional Grammar
Stefan Riezler | Tracy H. King | Richard Crouch | Annie Zaenen
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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The PARC 700 Dependency Bank
Tracy Holloway King | Richard Crouch | Stefan Riezler | Mary Dalrymple | Ronald M. Kaplan
Proceedings of 4th International Workshop on Linguistically Interpreted Corpora (LINC-03) at EACL 2003


Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques
Stefan Riezler | Tracy H. King | Ronald M. Kaplan | Richard Crouch | John T. Maxwell III | Mark Johnson
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics


On Interpreting F-Structures as UDRSs
Josef van Genabith | Richard Crouch
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics


Ellipsis and Quantification: A Substitutional Approach
Richard Crouch
Seventh Conference of the European Chapter of the Association for Computational Linguistics


Monotonic Semantic Interpretation
Hiyan Alshawi | Richard Crouch
30th Annual Meeting of the Association for Computational Linguistics