Search Results for author: Julia Kiseleva

Found 26 papers, 16 papers with code

Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback

no code implementations21 Apr 2023 Nikhil Mehta, Milagro Teruel, Patricio Figueroa Sanz, Xin Deng, Ahmed Hassan Awadallah, Julia Kiseleva

We explore multiple types of help players can give to the AI to guide it and analyze the impact of this help in AI behavior, resulting in performance improvements.

IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents

1 code implementation31 May 2022 Artem Zholus, Alexey Skrynnik, Shrestha Mohanty, Zoya Volovikova, Julia Kiseleva, Artur Szlam, Marc-Alexandre Coté, Aleksandr I. Panov

We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way.

reinforcement-learning Reinforcement Learning (RL)

PREME: Preference-based Meeting Exploration through an Interactive Questionnaire

1 code implementation5 May 2022 Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi

The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.

Supporting Complex Information-Seeking Tasks with Implicit Constraints

no code implementations2 May 2022 Ali Ahmadvand, Negar Arabzadeh, Julia Kiseleva, Patricio Figueroa Sanz, Xin Deng, Sujay Jauhar, Michael Gamon, Eugene Agichtein, Ned Friend, Aniruddha

Current interactive systems with natural language interface lack an ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences, e. g., "find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.


Summarization with Graphical Elements

1 code implementation15 Apr 2022 Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke

Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people.

Text Summarization

Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

1 code implementation EMNLP 2021 Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeffrey Dalton, Mikhail Burtsev

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response.

Learning to Decompose and Organize Complex Tasks

1 code implementation NAACL 2021 Yi Zhang, Sujay Kumar Jauhar, Julia Kiseleva, Ryen White, Dan Roth

Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.


Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems

1 code implementation30 Apr 2021 Ziming Li, Julia Kiseleva, Maarten de Rijke

The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.

Response Generation

DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues

no code implementations1 Mar 2021 Ziming Li, Dookun Park, Julia Kiseleva, Young-Bum Kim, Sungjin Lee

Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.

What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research

no code implementations NAACL 2022 Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke

Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.

Text Summarization

ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

3 code implementations23 Sep 2020 Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, Mikhail Burtsev

The main aim of the conversational systems is to return an appropriate answer in response to the user requests.

Optimizing Interactive Systems via Data-Driven Objectives

no code implementations19 Jun 2020 Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke, Ryen W. White

Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.

Guided Dialog Policy Learning without Adversarial Learning in the Loop

1 code implementation7 Apr 2020 Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao

Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.

Reinforcement Learning (RL)

How to Not Measure Disentanglement

no code implementations12 Oct 2019 Anna Sepliarskaia, Julia Kiseleva, Maarten de Rijke

We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition.

Disentanglement Transfer Learning +1

SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection

no code implementations16 Jun 2019 Jiahuan Pei, Arent Stienstra, Julia Kiseleva, Maarten de Rijke

Obtaining key information from a complex, long dialogue context is challenging, especially when different sources of information are available, e. g., the user's utterances, the system's responses, and results retrieved from a knowledge base (KB).

Task-Oriented Dialogue Systems

Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

1 code implementation9 Dec 2018 Ziming Li, Julia Kiseleva, Maarten de Rijke

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.

Dialogue Generation Imitation Learning +2

Learning Data-Driven Objectives to Optimize Interactive Systems

no code implementations17 Feb 2018 Ziming Li, Julia Kiseleva, Alekh Agarwal, Maarten de Rijke

Effective optimization is essential for interactive systems to provide a satisfactory user experience.

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