Dialog Systems
Related Papers
Combining Deictic Gestures and Natural Language for Referent Identification
In virtually all current natural-language dialog systems, users can only refer to objects by using linguistic descriptions. How- ever, in human face-to-face conversation, participants fre= quently use various sorts of …...
Resolving Referring Expressions in Conversational Dialogs for Natural User Interfaces
Unlike traditional over-the-phone spoken dialog systems (SDSs), modern dialog systems tend to have visual rendering on the device screen as an additional modal- ity to communicate the system’s response to …...
Generating Responses with a Specific Emotion in Dialog
AbstractIt is desirable for dialog systems to have capability to express specific emotions during a conversation, which has a direct, quantifiable impact on improvement of their usability and user satisfaction. …...
Topic Spotting using Hierarchical Networks with Self Attention
AbstractSuccess of deep learning techniques have renewed the interest in development of dialogue systems. However, current systems struggle to have consistent long term conversations with the users and fail to …...
Example-Driven Intent Prediction with Observers
AbstractA key challenge of dialog systems research is to effectively and efficiently adapt to new domains. A scalable paradigm for adaptation necessitates the development of generalizable models that perform well …...
Hierarchical Transformer for Task Oriented Dialog Systems
AbstractGenerative models for dialog systems have gained much interest because of the recent success of RNN and Transformer based models in tasks like question answering and summarization. Although the task …...
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks
AbstractAlthough coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been neglected. With the advancements …...
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity
AbstractHumans are increasingly interacting with machines through language, sometimes in contexts where the user may not know they are talking to a machine (like over the phone or a text …...
DiaBiz – an Annotated Corpus of Polish Call Center Dialogs
AbstractThis paper introduces DiaBiz, a large, annotated, multimodal corpus of Polish telephone conversations conducted in varied business settings, comprising 4036 call centre interactions from nine different domains, i.e. banking, energy …...
Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems
AbstractDialog systems are often designed or trained to output human-like responses. However, some responses may be impossible for a machine to truthfully say (e.g. “that movie made me cry”). Highly …...
Selective Differential Privacy for Language Modeling
AbstractWith the increasing applications of language models, it has become crucial to protect these models from leaking private information. Previous work has attempted to tackle this challenge by training RNN-based …...
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands
AbstractParaphrasing is often performed with less concern for controlled style conversion. Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner, which also matters with industrial …...