Nathaniel Weir


InFillmore: Frame-Guided Language Generation with Bidirectional Context
Jiefu Ou | Nathaniel Weir | Anton Belyy | Felix Yu | Benjamin Van Durme
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We propose a structured extension to bidirectional-context conditional language generation, or “infilling,” inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.


COD3S: Diverse Generation with Discrete Semantic Signatures
Nathaniel Weir | João Sedoc | Benjamin Van Durme
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models. Conditioned on an input, seq2seqs typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks. Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity. Though it is generally applicable, we apply to causal generation, the task of predicting a proposition’s plausible causes or effects. We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.