Search Results for author:
Found 14 papers, 3 papers with code
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
GO FIGURE: A Meta Evaluation of Factuality in Summarization
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
We describe the annotation process in detail and compare it with other similar evaluation systems.
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-Human Conversation Scenarios
In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task.
Zero-Shot Adaptive Transfer for Conversational Language Understanding
Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains.
Bag of Experts Architectures for Model Reuse in Conversational Language Understanding
Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants.
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
The New Yorker publishes a weekly captionless cartoon.
Heterogeneous Networks and Their Applications: Scientometrics, Name Disambiguation, and Topic Modeling
We present heterogeneous networks as a way to unify lexical networks with relational data.
