Search Results for author:
Found 17 papers, 3 papers with code
KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric
Intent discovery is crucial for both building new conversational agents and improving existing ones.
Towards Automatic Evaluation of Task-Oriented Dialogue Flows
Task-oriented dialogue systems rely on predefined conversation schemes (dialogue flows) often represented as directed acyclic graphs.
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker
We demonstrate the effectiveness of the re-ranker by applying it to two state-of-the-art text-to-SQL models, and achieve top 4 score on the Spider leaderboard at the time of writing this article.
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches.
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering
Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.
Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision
We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets.
Contextual Topic Modeling For Dialog Systems
On annotated data, we show that incorporating context and dialog acts leads to relative gains in topic classification accuracy by 35% and on unsupervised keyword detection recall by 11% for conversational interactions where topics frequently span multiple utterances.
Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks
We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries.
Contextual Language Model Adaptation for Conversational Agents
Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Real-time Road Traffic Information Detection Through Social Media
In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed.
On Evaluating and Comparing Open Domain Dialog Systems
In this paper, we propose a comprehensive evaluation strategy with multiple metrics designed to reduce subjectivity by selecting metrics which correlate well with human judgement.
Conversational AI: The Science Behind the Alexa Prize
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational
Topic-based Evaluation for Conversational Bots
Dialog evaluation is a challenging problem, especially for non task-oriented dialogs where conversational success is not well-defined.
