activity
20122025
most citedLearning STRIPS Operators from Noisy and Incomplete Observations

64 citations · 78 across the 12 of their papers we have counts for

collaborators
Showing 2021Show all

6 papers · 1 filter

cs.CL2021

Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

Nikita Moghe, Mark Steedman, Alexandra Birch

Recent progress in task-oriented neural dialogue systems is largely focused on a handful of languages, as annotation of training data is tedious and expensive. Machine translation…

cs.CL2021

Blindness to Modality Helps Entailment Graph Mining

Liane Guillou, Sander Bijl de Vroe, Mark Johnson +1

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also b…

cs.CL2021

Incorporating Temporal Information in Entailment Graph Mining

Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini +2

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct even…

cs.CL20217 cited

Modality and Negation in Event Extraction

Sander Bijl de Vroe, Liane Guillou, Miloš Stanojević +2

Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the…

cs.CL2021

Prosodic segmentation for parsing spoken dialogue

Elizabeth Nielsen, Mark Steedman, Sharon Goldwater

Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with pa…

cs.CL2021

Multivalent Entailment Graphs for Question Answering

Nick McKenna, Liane Guillou, Mohammad Javad Hosseini +3

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailme…