Search Results for author: Tsvetomila Mihaylova

Found 11 papers, 7 papers with code

Discrete Latent Structure in Neural Networks

no code implementations18 Jan 2023 Vlad Niculae, Caio F. Corro, Nikita Nangia, Tsvetomila Mihaylova, André F. T. Martins

Many types of data from fields including natural language processing, computer vision, and bioinformatics, are well represented by discrete, compositional structures such as trees, sequences, or matchings.

Modeling Structure with Undirected Neural Networks

1 code implementation8 Feb 2022 Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins

In this paper, we combine the representational strengths of factor graphs and of neural networks, proposing undirected neural networks (UNNs): a flexible framework for specifying computations that can be performed in any order.

Dependency Parsing Image Classification

Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning

1 code implementation EMNLP 2020 Tsvetomila Mihaylova, Vlad Niculae, André F. T. Martins

Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously uncovering linguistic insights about the data.

Automatic Fact-Checking Using Context and Discourse Information

1 code implementation4 Aug 2019 Pepa Atanasova, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Georgi Karadzhov, Tsvetomila Mihaylova, Mitra Mohtarami, James Glass

We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.

Fact Checking

Latent Structure Models for Natural Language Processing

no code implementations ACL 2019 Andr{\'e} F. T. Martins, Tsvetomila Mihaylova, Nikita Nangia, Vlad Niculae

Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines.

Language Modelling Machine Translation +4

Scheduled Sampling for Transformers

3 code implementations ACL 2019 Tsvetomila Mihaylova, André F. T. Martins

In the Transformer model, unlike the RNN, the generation of a new word attends to the full sentence generated so far, not only to the last word, and it is not straightforward to apply the scheduled sampling technique.


Fact Checking in Community Forums

3 code implementations8 Mar 2018 Tsvetomila Mihaylova, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Mitra Mohtarami, Georgi Karadzhov, James Glass

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information.

Community Question Answering Fact Checking

Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums

1 code implementation RANLP 2017 Preslav Nakov, Tsvetomila Mihaylova, Llu{\'\i}s M{\`a}rquez, Yashkumar Shiroya, Ivan Koychev

We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted.

Community Question Answering Information Retrieval

The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation

2 code implementations12 Jul 2017 Georgi Karadjov, Tsvetomila Mihaylova, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov

Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics.

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