596 citations · 881 across the 24 of their papers we have counts for
6 papers · 1 filter
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Chunting Zhou, Graham Neubig, Jiatao Gu +4
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the inpu…
A Multitask Learning Approach for Diacritic Restoration
Sawsan Alqahtani, Ajay Mishra, Mona Diab
In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possib…
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
Esin Durmus, He He, Mona Diab
Neural abstractive summarization models are prone to generate content inconsistent with the source document, i.e. unfaithful. Existing automatic metrics do not capture such mistake…
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies
Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask lear…
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking
Christopher Hidey, Tuhin Chakrabarty, Tariq Alhindi +4
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extra…
Learning to Classify Intents and Slot Labels Given a Handful of Examples
Jason Krone, Yi Zhang, Mona Diab
Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training example…