Search Results for author: Gokhan Tur

Found 28 papers, 10 papers with code

TEACh: Task-driven Embodied Agents that Chat

1 code implementation1 Oct 2021 Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava, Patrick Lange, Anjali Narayan-Chen, Spandana Gella, Robinson Piramuthu, Gokhan Tur, Dilek Hakkani-Tur

Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes.

Dialogue Understanding

Are We There Yet? Learning to Localize in Embodied Instruction Following

no code implementations9 Jan 2021 Shane Storks, Qiaozi Gao, Govind Thattai, Gokhan Tur

Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs.

Object Detection

Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents

no code implementations29 Dec 2020 Yi-Chia Wang, Alexandros Papangelis, Runze Wang, Zhaleh Feizollahi, Gokhan Tur, Robert Kraut

The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.

Interactive Teaching for Conversational AI

no code implementations2 Dec 2020 Qing Ping, Feiyang Niu, Govind Thattai, Joel Chengottusseriyil, Qiaozi Gao, Aishwarya Reganti, Prashanth Rajagopal, Gokhan Tur, Dilek Hakkani-Tur, Prem Nataraja

Current conversational AI systems aim to understand a set of pre-designed requests and execute related actions, which limits them to evolve naturally and adapt based on human interactions.

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

2 code implementations21 Nov 2020 Weixin Liang, Feiyang Niu, Aishwarya Reganti, Govind Thattai, Gokhan Tur

We show that LRTA makes a step towards truly understanding the question while the state-of-the-art model tends to learn superficial correlations from the training data.

Answer Generation Question Answering +2

Warped Language Models for Noise Robust Language Understanding

no code implementations3 Nov 2020 Mahdi Namazifar, Gokhan Tur, Dilek Hakkani Tür

The insertion and drop modification of the input text during training of WLM resemble the types of noise due to Automatic Speech Recognition (ASR) errors, and as a result WLMs are likely to be more robust to ASR noise.

Automatic Speech Recognition Natural Language Understanding +1

Improving Embedding Extraction for Speaker Verification with Ladder Network

no code implementations20 Mar 2020 Fei Tao, Gokhan Tur

Speaker verification is an established yet challenging task in speech processing and a very vibrant research area.

Speaker Verification

Joint Contextual Modeling for ASR Correction and Language Understanding

no code implementations28 Jan 2020 Yue Weng, Sai Sumanth Miryala, Chandra Khatri, Runze Wang, Huaixiu Zheng, Piero Molino, Mahdi Namazifar, Alexandros Papangelis, Hugh Williams, Franziska Bell, Gokhan Tur

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.

14 Automatic Speech Recognition +1

Plato Dialogue System: A Flexible Conversational AI Research Platform

4 code implementations17 Jan 2020 Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri, Yi-Chia Wang, Piero Molino, Gokhan Tur

Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.

Spoken Dialogue Systems

Flexibly-Structured Model for Task-Oriented Dialogues

1 code implementation WS 2019 Lei Shu, Piero Molino, Mahdi Namazifar, Hu Xu, Bing Liu, Huaixiu Zheng, Gokhan Tur

It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.

Task-Oriented Dialogue Systems Text Generation

OCC: A Smart Reply System for Efficient In-App Communications

no code implementations18 Jul 2019 Yue Weng, Huaixiu Zheng, Franziska Bell, Gokhan Tur

Our system consists of two major components: intent detection and reply retrieval, which are very different from standard smart reply systems where the task is to directly predict a reply.

Intent Detection

User Modeling for Task Oriented Dialogues

no code implementations11 Nov 2018 Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur, Pararth Shah

We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal.

Dialogue State Tracking Task-Oriented Dialogue Systems

Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

1 code implementation NAACL 2018 Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, Larry Heck

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.

Dialogue State Tracking Imitation Learning +2

End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning

no code implementations29 Nov 2017 Bing Liu, Gokhan Tur, Dilek Hakkani-Tur, Pararth Shah, Larry Heck

We show that deep RL based optimization leads to significant improvement on task success rate and reduction in dialogue length comparing to supervised training model.


Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

1 code implementation7 Jul 2017 Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur, Larry Heck

While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems.

Frame Semantic Parsing +1

Sequential Dialogue Context Modeling for Spoken Language Understanding

1 code implementation WS 2017 Ankur Bapna, Gokhan Tur, Dilek Hakkani-Tur, Larry Heck

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.

Frame Goal-Oriented Dialogue Systems +1

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