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Found 11 papers, 7 papers with code
Discrete Latent Structure in Neural Networks
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
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.
SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
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
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.
Latent Structure Models for Natural Language Processing
Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines.
Scheduled Sampling for Transformers
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.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
For subtask A, all systems improved over the majority class baseline.
Fact Checking in Community Forums
Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information.
Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums
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.
The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation
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.
