activity
20162022
most citedFrom Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap

16 citations · 42 across the 8 of their papers we have counts for

collaborators

13 papers

cs.CL20221 cited

Context-Situated Pun Generation

Jiao Sun, Anjali Narayan-Chen, Shereen Oraby +5

Previous work on pun generation commonly begins with a given pun word (a pair of homophones for heterographic pun generation and a polyseme for homographic pun generation) and seek…

cs.CL2022

Towards Textual Out-of-Domain Detection without In-Domain Labels

Di Jin, Shuyang Gao, Seokhwan Kim +2

In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a chall…

cs.CL2021

Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems

Di Jin, Shuyang Gao, Seokhwan Kim +2

Most prior work on task-oriented dialogue systems is restricted to supporting domain APIs. However, users may have requests that are out of the scope of these APIs. This work focus…

stat.ML20219 cited

Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE

Junya Chen, Zhe Gan, Xuan Li +10

InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years. However, these contrastive schemes are notoriously resource de…

cs.CL20213 cited

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

Anish Acharya, Suranjit Adhikari, Sanchit Agarwal +28

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Trai…

cs.CL202016 cited

From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap

Shuyang Gao, Sanchit Agarwal, Tagyoung Chung +2

Dialogue state tracking (DST) is at the heart of task-oriented dialogue systems. However, the scarcity of labeled data is an obstacle to building accurate and robust state tracking…