45 citations · 71 across the 23 of their papers we have counts for
28 papers
SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking
Guransh Singh, Vishwajeet Kumar, Arkadeep Acharya +3
Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce SearchWiki, a harness framework tha…
Spatial Matryoshka Training for Multi-Granularity Visual Document Retrieval
Trishan Singha Roy, Arkadeep Acharya, Vishwajeet Kumar +2
Multi-modal late-interaction retrievers achieve strong retrieval on visually rich documents by representing each page as per patch embeddings and matching at the token level. Howev…
CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval
Adnan Qidwai, Anand Eswaran, Sonam Mishra +2
RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.g., synthetic queries or summaries generated from corpus chunks) that are subsequ…
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
Yatin Nandwani, Vineet Kumar, Dinesh Raghu +2
A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlyi…
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
Hui Wan, Siva Sankalp Patel, J. William Murdock +2
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no m…
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
Pengshan Cai, Hui Wan, Fei Liu +3
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bo…
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings
Gaurav Pandey, Danish Contractor, Sachindra Joshi
Embedding-based approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. These approaches are scalable, but fail to account for t…
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
Benjamin Sznajder, Chulaka Gunasekara, Guy Lev +3
Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organi…
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…
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
Song Feng, Hui Wan, Chulaka Gunasekara +3
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users,…
Conversational Document Prediction to Assist Customer Care Agents
Jatin Ganhotra, Haggai Roitman, Doron Cohen +6
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs. We study the task of predicting the documents th…
Effects of Naturalistic Variation in Goal-Oriented Dialog
Jatin Ganhotra, Robert Moore, Sachindra Joshi +1
Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations. Most datasets are c…
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions
Biswesh Mohapatra, Gaurav Pandey, Danish Contractor +1
Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd wor…
Mask & Focus: Conversation Modelling by Learning Concepts
Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the…
Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns
Nikhil Verma, Abhishek Sharma, Dhiraj Madan +3
Neural Conversational QA tasks like ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARCQA ta…
Unsupervised Learning of Interpretable Dialog Models
Dhiraj Madan, Dinesh Raghu, Gaurav Pandey +1
Recently several deep learning based models have been proposed for end-to-end learning of dialogs. While these models can be trained from data without the need for any additional a…