Papers with meaning representation
Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (D18-1)
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| Challenge: | Existing methods to extract information from text do not capture disparity between demographic groups. |
| Approach: | They propose a task of Textual Analogy Parsing to model higher-order meanings by comparing poverty rates between different demographic groups. |
| Outcome: | The proposed model can be used to generate graphs from quantitative text. |
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation (N19-2)
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| Challenge: | Using a pointer-generator network, we model the reference resolution task as a dialogue context-aware user query reformulation task. |
| Approach: | They propose a pointer-generator network and a novel multi-task learning setup to model dialogue state tracking and referring expression resolution tasks using a dialogue context-aware user query reformulation task. |
| Outcome: | The proposed model improves absolute F1 on internal and public benchmarks. |
The Alexa Meaning Representation Language (N18-3)
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Thomas Kollar, Danielle Berry, Lauren Stuart, Karolina Owczarzak, Tagyoung Chung, Lambert Mathias, Michael Kayser, Bradford Snow, Spyros Matsoukas
| Challenge: | a new meaning representation language for spoken language is introduced for Alexa . AMRL provides a common representation for how people communicate in spoken language . there is no mechanism to represent ambiguity, forcing the choice of a fixed interpretation for ambiguous utterances. |
| Approach: | They introduce a meaning representation for spoken language, the Alexa meaning represent language . they use a spoken language dataset to collect a sample of utterances from eight domains . |
| Outcome: | The proposed representation provides a common representation for spoken language understanding . it supports cross-domain queries, fine-grained types, complex utterances and composition . the proposed representation was released to developers at a trade show in 2016 . |
Benchmarking Meaning Representations in Neural Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing work on meaning representations is not comprehensively evaluated due to the lack of readily-available execution engines. |
| Approach: | They propose a unified benchmark on meaning representations by integrating existing semantic parsing datasets, completing the missing logical forms, and implementing the missing execution engines. |
| Outcome: | The proposed benchmark combines existing parsing datasets, completes missing logical forms, and implements missing execution engines. |
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area. |
| Approach: | They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach. |
| Outcome: | The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues. |
Abstract Meaning Representation for Gesture (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation (AMR) is an annotated graphbased representation that expresses the meaning of a sentence in terms of its predicate-argument structure. |
| Approach: | They propose an extension to Abstract Meaning Representation (AMR) that captures the meaning of gesture. |
| Outcome: | The proposed model is more challenging than standard AMR while integrating meaningful elements unique to gesture. |
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing (2022.acl-long)
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| Challenge: | Semantic parsers struggle to generalize to examples with unseen combinations of seen rules from the training set. |
| Approach: | They propose a general framework to produce semantic parses by predicting node labels for a complete multi-layer input-aligned graph. |
| Outcome: | The proposed framework produces better generalizations than the baseline framework . it produces representations directly as a graph and not as sequences . |
Surface Realisation Using Full Delexicalisation (D19-1)
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| Challenge: | Existing approaches to surface realisation model word ordering, morphological inflection and contraction generation are evaluated on 10 languages covered by the SR'18 shared task. |
| Approach: | They propose a modular approach which models each of these components separately and an analysis of the differences in word ordering performance across languages. |
| Outcome: | The proposed model is compared with existing models on 10 languages covered by the SR'18 shared task. |
Broad-Coverage Semantic Parsing as Transduction (D19-1)
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| Challenge: | Existing approaches to broad-coverage semantic parsing are not applicable to all frameworks because of the lack of explicit alignments between tokens in the sentence and nodes in the semantic graph. |
| Approach: | They propose a transduction parsing paradigm that unifies different broad-coverage semantic parsers into a paradigm that leverages multiple attention mechanisms to build meaning representation. |
| Outcome: | The proposed approach improves state-of-the-art on AMR, SDP and UCCA and is competitive with the state- of-the art on SDP. |
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies (2020.emnlp-main)
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| Challenge: | Using task-oriented dialogue generation benchmarks, we compare the effect of four input linearization strategies on controllability and faithfulness. |
| Approach: | They compare the effect of four input linearization strategies on controllability and faithfulness . they also evaluate how a phrase-based data augmentation method can improve performance . |
| Outcome: | The proposed model can generate utterances whose phrases follow the order of the provided plan. |