Search Results for author: Zornitsa Kozareva

Found 29 papers, 6 papers with code

SoDA: On-device Conversational Slot Extraction

no code implementations SIGDIAL (ACL) 2021 Sujith Ravi, Zornitsa Kozareva

We propose a novel on-device neural sequence labeling model which uses embedding-free projections and character information to construct compact word representations to learn a sequence model using a combination of bidirectional LSTM with self-attention and CRF.

Few-shot Learning with Multilingual Language Models

1 code implementation20 Dec 2021 Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li

In this work, we train multilingual autoregressive language models on a balanced corpus covering a diverse set of languages, and study their few- and zero-shot learning capabilities in a wide range of tasks.

Few-Shot Learning Hate Speech Detection +4

Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs

1 code implementation26 Nov 2021 Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer

In this paper, we discuss approaches to detecting when models have beliefs about the world, and we improve on methods for updating model beliefs to be more truthful, with a focus on methods based on learned optimizers or hypernetworks.

Fixed Support Tree-Sliced Wasserstein Barycenter

no code implementations8 Sep 2021 Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada

By contrast, the Wasserstein distance on a tree, called the tree-Wasserstein distance, can be computed in linear time and allows for the fast comparison of a large number of distributions.

ProFormer: Towards On-Device LSH Projection Based Transformers

no code implementations EACL 2021 Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva

At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT.

General Classification Text Classification

Environment-agnostic Multitask Learning for Natural Language Grounded Navigation

1 code implementation ECCV 2020 Xin Eric Wang, Vihan Jain, Eugene Ie, William Yang Wang, Zornitsa Kozareva, Sujith Ravi

Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.

Vision-Language Navigation

SemEval-2013 Task 2: Sentiment Analysis in Twitter

no code implementations SEMEVAL 2013 Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, Theresa Wilson

To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.

Sentiment Analysis

Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning

no code implementations25 Sep 2019 Xin Wang, Vihan Jain, Eugene Ie, William Wang, Zornitsa Kozareva, Sujith Ravi

Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.

Vision-Language Navigation

On-Device Text Representations Robust To Misspellings via Projections

no code implementations EACL 2021 Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva

Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory.

Text Classification Word Embeddings

Transferable Neural Projection Representations

2 code implementations NAACL 2019 Chinnadhurai Sankar, Sujith Ravi, Zornitsa Kozareva

Neural word representations are at the core of many state-of-the-art natural language processing models.

Learning Steady-States of Iterative Algorithms over Graphs

no code implementations ICML 2018 Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song

Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.

Variational Reasoning for Question Answering with Knowledge Graph

1 code implementation12 Sep 2017 Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.

Knowledge Graphs Question Answering +1

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