Papers by Lasha Abzianidze
What can we learn from Semantic Tagging? (D18-1)
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| Challenge: | a recent study shows that multi-task learning improves performance of NLP tasks by exploiting similarities between tasks. |
| Approach: | They employ semantic tagging as an auxiliary task for three NLP tasks . they compare full neural network sharing, partial neural network shared and learning what to share . |
| Outcome: | The proposed model improves for part-of-speech tagging, universal dependency parsing and natural language inference. |
Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference (2020.starsem-1)
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| Challenge: | a logic-based approach to Natural Language Inference is becoming less and less common . a new method uses semantic relations to abduct sentences from data . |
| Approach: | They propose a method to reverse a theorem-proving procedure to abduct semantic relations from data. |
| Outcome: | The proposed method improves the performance of the theorem prover on the SICK dataset by 1.4% while maintaining high precision (>94%) |
Evaluating Scoped Meaning Representations (L18-1)
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| Challenge: | Semantic parsing offers many opportunities to improve natural language understanding . current research on open-domain semantic parsers focuses on supervised learning methods . |
| Approach: | They propose a semantically annotated parallel corpus for English, German, Italian, and Dutch . they use a matching tool to evaluate scoped meaning representations to match clauses . |
| Outcome: | The proposed method captures the semantics of negation, modals, quantification, and presupposition triggers . it compares scoped meaning representations to gold standard parsers and finds improvements . |