Search Results for author: Spandana Gella

Found 20 papers, 8 papers with code

Assisting Composition of Email Responses: a Topic Prediction Approach

no code implementations7 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.

Sentence

Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings

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.

Image Retrieval Retrieval +1

An Analysis of Action Recognition Datasets for Language and Vision Tasks

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.

Action Recognition Image Retrieval +2

Image Pivoting for Learning Multilingual Multimodal Representations

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.

Image Retrieval Semantic Textual Similarity

Cross-lingual Visual Verb Sense Disambiguation

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.

Machine Translation Translation

Neural Word Decomposition Models for Abusive Language Detection

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.

Abusive Language

Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions

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.

Contrastive Learning Multi-Task Learning +2

An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

1 code implementation14 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.

Multi-Task Learning Natural Language Inference +1

TEACh: Task-driven Embodied Agents that Chat

3 code implementations1 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

Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation

1 code implementation11 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.

Response Generation

Analyzing the Limits of Self-Supervision in Handling Bias in Language

no code implementations16 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.

Dialog Acts for Task-Driven Embodied Agents

no code implementations26 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.

Natural Language Understanding

DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

1 code implementation20 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.

Response Generation

Using In-Context Learning to Improve Dialogue Safety

no code implementations2 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.

In-Context Learning Re-Ranking +1

Multimodal Contextualized Plan Prediction for Embodied Task Completion

no code implementations10 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.

Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions

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.

Dialog Acts for Task Driven Embodied Agents

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.

Natural Language Understanding

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