Choh Man Teng


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The Role of Semantic Parsing in Understanding Procedural Text
Hossein Rajaby Faghihi | Parisa Kordjamshidi | Choh Man Teng | James Allen
Findings of the Association for Computational Linguistics: EACL 2023

In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help reasoning over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into state-of-the-art neural models to conduct procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.


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A Broad-Coverage Deep Semantic Lexicon for Verbs
James Allen | Hannah An | Ritwik Bose | Will de Beaumont | Choh Man Teng
Proceedings of the Twelfth Language Resources and Evaluation Conference

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.


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Putting Semantics into Semantic Roles
James Allen | Choh Man Teng
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

While there have been many proposals for theories of semantic roles over the years, these models are mostly justified by intuition and the only evaluation methods have been inter-annotator agreement. We explore three different ideas for providing more rigorous theories of semantic roles. These ideas give rise to more objective criteria for designing role sets, and lend themselves to some experimental evaluation. We illustrate the discussion by examining the semantic roles in TRIPS.

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Building and Learning Structures in a Situated Blocks World Through Deep Language Understanding
Ian Perera | James Allen | Choh Man Teng | Lucian Galescu
Proceedings of the First International Workshop on Spatial Language Understanding

We demonstrate a system for understanding natural language utterances for structure description and placement in a situated blocks world context. By relying on a rich, domain-specific adaptation of a generic ontology and a logical form structure produced by a semantic parser, we obviate the need for an intermediate, domain-specific representation and can produce a reasoner that grounds and reasons over concepts and constraints with real-valued data. This linguistic base enables more flexibility in interpreting natural language expressions invoking intrinsic concepts and features of structures and space. We demonstrate some of the capabilities of a system grounded in deep language understanding and present initial results in a structure learning task.

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A Situated Dialogue System for Learning Structural Concepts in Blocks World
Ian Perera | James Allen | Choh Man Teng | Lucian Galescu
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning. The concept to learn is provided to the user through a set of positive and negative visual examples, from which the user determines the underlying constraints to be provided to the system in natural language. The system in turn asks questions about demonstrated examples and simulates new examples to check its knowledge and verify the user’s description is complete. We find that this task is non-trivial for users and generates natural language that is varied yet understood by our deep language understanding architecture.

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Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model
Lucian Galescu | Choh Man Teng | James Allen | Ian Perera
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell.


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Compositionality in Verb-Particle Constructions
Archna Bhatia | Choh Man Teng | James Allen
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and then present a set of heuristics for classifying specific instances as compositional or not. We then identify a small set of general sense classes for particles when used compositionally and discuss the resulting lexical representations that are being added to the lexicon. By treating VPCs as compositional whenever possible, we attain broad coverage in a compact way, and also enable interpretations of novel VPC usages not explicitly present in the lexicon.


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Complex Event Extraction using DRUM
James Allen | Will de Beaumont | Lucian Galescu | Choh Man Teng
Proceedings of BioNLP 15


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Automatically Deriving Event Ontologies for a CommonSense Knowledge Base
James Allen | Will de Beaumont | Lucian Galescu | Jansen Orfan | Mary Swift | Choh Man Teng
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers