no code implementations • EMNLP 2021 • Arjun Akula, Spandana Gella, Keze Wang, Song-Chun Zhu, Siva Reddy
Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.
no code implementations • SIGDIAL (ACL) 2022 • Spandana Gella, Aishwarya Padmakumar, Patrick Lange, Dilek Hakkani-Tur
Embodied agents need to be able to interact in natural language – understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users.
1 code implementation • 20 May 2023 • Chao Zhao, Spandana Gella, Seokhwan Kim, Di Jin, Devamanyu Hazarika, Alexandros Papangelis, Behnam Hedayatnia, Mahdi Namazifar, Yang Liu, Dilek Hakkani-Tur
We hope this task and dataset can promote further research on TOD and subjective content understanding.
no code implementations • 10 May 2023 • Mert İnan, Aishwarya Padmakumar, Spandana Gella, Patrick Lange, Dilek Hakkani-Tur
Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks.
no code implementations • 2 Feb 2023 • Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tür
For instance, using automatic evaluation, we find our best fine-tuned baseline only generates safe responses to unsafe dialogue contexts from DiaSafety 4. 04% more than our approach.
1 code implementation • 20 Dec 2022 • Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur
These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent.
no code implementations • 26 Sep 2022 • Spandana Gella, Aishwarya Padmakumar, Patrick Lange, Dilek Hakkani-Tur
Embodied agents need to be able to interact in natural language understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be effective at successfully accomplishing tasks for a wide range of users.
no code implementations • 16 Dec 2021 • Lisa Bauer, Karthik Gopalakrishnan, Spandana Gella, Yang Liu, Mohit Bansal, Dilek Hakkani-Tur
We define three broad classes of task descriptions for these tasks: statement, question, and completion, with numerous lexical variants within each class.
1 code implementation • 11 Oct 2021 • Sashank Santhanam, Behnam Hedayatnia, Spandana Gella, Aishwarya Padmakumar, Seokhwan Kim, Yang Liu, Dilek Hakkani-Tur
We demonstrate the benefit of our Conv-FEVER dataset by showing that the models trained on this data perform reasonably well to detect factually inconsistent responses with respect to the provided knowledge through evaluation on our human annotated data.
3 code implementations • 1 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.
1 code implementation • 14 Jul 2020 • Lifu Tu, Garima Lalwani, Spandana Gella, He He
Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset.
1 code implementation • ACL 2020 • Arjun R. Akula, Spandana Gella, Yaser Al-Onaizan, Song-Chun Zhu, Siva Reddy
To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn't.
no code implementations • WS 2019 • Sravan Babu Bodapati, Spandana Gella, Kasturi Bhattacharjee, Yaser Al-Onaizan
User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc.
no code implementations • ACL 2019 • Shruti Palaskar, Jindrich Libovický, Spandana Gella, Florian Metze
In this paper, we study abstractive summarization for open-domain videos.
Ranked #1 on Text Summarization on How2
1 code implementation • NAACL 2019 • Spandana Gella, Desmond Elliott, Frank Keller
We extend this line of work to the more challenging task of cross-lingual verb sense disambiguation, introducing the MultiSense dataset of 9, 504 images annotated with English, German, and Spanish verbs.
no code implementations • EMNLP 2017 • Spandana Gella, Rico Sennrich, Frank Keller, Mirella Lapata
In this paper we propose a model to learn multimodal multilingual representations for matching images and sentences in different languages, with the aim of advancing multilingual versions of image search and image understanding.
no code implementations • ACL 2017 • Spandana Gella, Frank Keller
A large amount of recent research has focused on tasks that combine language and vision, resulting in a proliferation of datasets and methods.
1 code implementation • NAACL 2016 • Spandana Gella, Mirella Lapata, Frank Keller
We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i. e., the one that describes the action depicted in the image.
no code implementations • 7 Oct 2015 • Spandana Gella, Marc Dymetman, Jean Michel Renders, Sriram Venkatapathy
The experimental results on a large email collection from a contact center in the tele- com domain show that the proposed ap- proach is effective in predicting the best topic of the agent's next sentence.