45 citations · 71 across the 23 of their papers we have counts for
9 papers · 1 filter
DG2: Data Augmentation Through Document Grounded Dialogue Generation
Qingyang Wu, Song Feng, Derek Chen +3
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation. Especially in document-ground…
TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder +3
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the…
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
Song Feng, Siva Sankalp Patel, Hui Wan +1
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling a…
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs
Dinesh Raghu, Atishya Jain, Mausam +1
End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB). Inferring those KB entities that are most relevant for…
End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs
Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi +1
We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their…
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
Jatin Ganhotra, Samuel Thomas, Hong-Kwang J. Kuo +4
End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation…
Summary Grounded Conversation Generation
Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder +2
Many conversation datasets have been constructed in the recent years using crowdsourcing. However, the data collection process can be time consuming and presents many challenges to…
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Hanjie Chen, Song Feng, Jatin Ganhotra +4
Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural netwo…
Does Dialog Length matter for Next Response Selection task? An Empirical Study
Jatin Ganhotra, Sachindra Joshi
In the last few years, the release of BERT, a multilingual transformer based model, has taken the NLP community by storm. BERT-based models have achieved state-of-the-art results o…