Search Results for author:
Found 17 papers, 7 papers with code
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations
We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language model (LLM).
Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations
We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
Integrating Dialog History into End-to-End Spoken Language Understanding Systems
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 independently.
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
Does Dialog Length matter for Next Response Selection task? An Empirical Study
However, there has been little to no research on the impact of this limitation with respect to dialog tasks.
Effects of Naturalistic Variation in Goal-Oriented Dialog
Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations.
Conversational Document Prediction to Assist Customer Care Agents
A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users' needs.
Agent Assist through Conversation Analysis
Customer support agents play a crucial role as an interface between an organization and its end-users.
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently.
Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e. g. flight booking, hotel reservation, technical support, student advising etc.
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation.
A Large-Scale Corpus for Conversation Disentanglement
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting.
Knowledge-based end-to-end memory networks
End-to-end dialog systems have become very popular because they hold the promise of learning directly from human to human dialog interaction.
NE-Table: A Neural key-value table for Named Entities
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities
Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs).
