Dilek Hakkani-Tur

Also published as: Dilek Hakkani-Tür, D. Hakkani-Tur


2018

pdf pdf bib
Deep Learning for Dialogue Systems
Yun-Nung Chen | Asli Celikyilmaz | Dilek Hakkani-Tür

pdf pdf bib
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking
Abhinav Rastogi | Raghav Gupta | Dilek Hakkani-Tur

This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.

pdf pdf bib
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems
Bing Liu | Gokhan Tür | Dilek Hakkani-Tür | Pararth Shah | Larry Heck

In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. Efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback. We design a neural network based task-oriented dialogue agent that can be optimized end-to-end with the proposed learning method. Experimental results show that our end-to-end dialogue agent can learn effectively from the mistake it makes via imitation learning from user teaching. Applying reinforcement learning with user feedback after the imitation learning stage further improves the agent’s capability in successfully completing a task.

pdf pdf bib
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning
Pararth Shah | Dilek Hakkani-Tür | Bing Liu | Gokhan Tür

End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.

2017

pdf pdf bib
Deep Learning for Dialogue Systems
Yun-Nung Chen | Asli Celikyilmaz | Dilek Hakkani-Tür

In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw.

pdf pdf bib
Sequential Dialogue Context Modeling for Spoken Language Understanding
Ankur Bapna | Gokhan Tür | Dilek Hakkani-Tür | Larry Heck

Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.

2016

pdf pdf bib
AIMU: Actionable Items for Meeting Understanding
Yun-Nung Chen | Dilek Hakkani-Tür

With emerging conversational data, automated content analysis is needed for better data interpretation, so that it is accurately understood and can be effectively integrated and utilized in various applications. ICSI meeting corpus is a publicly released data set of multi-party meetings in an organization that has been released over a decade ago, and has been fostering meeting understanding research since then. The original data collection includes transcription of participant turns as well as meta-data annotations, such as disfluencies and dialog act tags. This paper presents an extended set of annotations for the ICSI meeting corpus with a goal of deeply understanding meeting conversations, where participant turns are annotated by actionable items that could be performed by an automated meeting assistant. In addition to the user utterances that contain an actionable item, annotations also include the arguments associated with the actionable item. The set of actionable items are determined by aligning human-human interactions to human-machine interactions, where a data annotation schema designed for a virtual personal assistant (human-machine genre) is adapted to the meetings domain (human-human genre). The data set is formed by annotating participants’ utterances in meetings with potential intents/actions considering their contexts. The set of actions target what could be accomplished by an automated meeting assistant, such as taking a note of action items that a participant commits to, or finding emails or topic related documents that were mentioned during the meeting. A total of 10 defined intents/actions are considered as actionable items in meetings. Turns that include actionable intents were annotated for 22 public ICSI meetings, that include a total of 21K utterances, segmented by speaker turns. Participants’ spoken turns, possible actions along with associated arguments and their vector representations as computed by convolutional deep structured semantic models are included in the data set for future research. We present a detailed statistical analysis of the data set and analyze the performance of applying convolutional deep structured semantic models for an actionable item detection task. The data is available at http://research.microsoft.com/ projects/meetingunderstanding/.

2015

pdf pdf bib
Keynote: Graph-based Approaches for Spoken Language Understanding
Dilek Hakkani-Tur

2014

pdf pdf bib
Resolving Referring Expressions in Conversational Dialogs for Natural User Interfaces
Asli Celikyilmaz | Zhaleh Feizollahi | Dilek Hakkani-Tur | Ruhi Sarikaya

2013

pdf pdf bib
Semi-Supervised Semantic Tagging of Conversational Understanding using Markov Topic Regression
Asli Celikyilmaz | Dilek Hakkani-Tur | Gokhan Tur | Ruhi Sarikaya

2012

pdf pdf bib
A Joint Model for Discovery of Aspects in Utterances
Asli Celikyilmaz | Dilek Hakkani-Tur

pdf pdf bib
Mining Search Query Logs for Spoken Language Understanding
Dilek Hakkani-Tür | Gokhan Tür | Asli Celikyilmaz

2011

pdf pdf bib
Discovery of Topically Coherent Sentences for Extractive Summarization
Asli Celikyilmaz | Dilek Hakkani-Tür

2010

pdf pdf bib
A Hybrid Hierarchical Model for Multi-Document Summarization
Asli Celikyilmaz | Dilek Hakkani-Tur

pdf pdf bib
LDA Based Similarity Modeling for Question Answering
Asli Celikyilmaz | Dilek Hakkani-Tur | Gokhan Tur

pdf pdf bib
A Graph-Based Semi-Supervised Learning for Question Semantic Labeling
Asli Celikyilmaz | Dilek Hakkani-Tur

2009

pdf pdf bib
Anchored Speech Recognition for Question Answering
Sibel Yaman | Gokhan Tur | Dimitra Vergyri | Dilek Hakkani-Tur | Mary Harper | Wen Wang

pdf pdf bib
Who, What, When, Where, Why? Comparing Multiple Approaches to the Cross-Lingual 5W Task
Kristen Parton | Kathleen R. McKeown | Bob Coyne | Mona T. Diab | Ralph Grishman | Dilek Hakkani-Tür | Mary Harper | Heng Ji | Wei Yun Ma | Adam Meyers | Sara Stolbach | Ang Sun | Gokhan Tur | Wei Xu | Sibel Yaman

2005

pdf pdf bib
Using Semantic and Syntactic Graphs for Call Classification
Dilek Hakkani-Tür | Gokhan Tur | Ananlada Chotimongkol

2004

pdf pdf bib
Bootstrapping Spoken Dialog Systems with Data Reuse
Guiseppe Di Fabbrizio | Gokhan Tur | Dilek Hakkani-Tür

pdf pdf bib
Mining Spoken Dialogue Corpora for System Evaluation and Modelin
Frederic Bechet | Giuseppe Riccardi | Dilek Hakkani-Tur

2001

pdf pdf bib
Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation
G. Tur | D. Hakkani-Tur | A. Stolcke | E. Shriberg