Tahira Naseem

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

2019 · arXiv (Cornell University)

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments …

CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases

2022 · arXiv (Cornell University)

Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To this end, we propose a …

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency

2023 · arXiv (Cornell University)

We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as a form of self-learning where the semantic parser being trained is …