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Found 14 papers, 8 papers with code
Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking
Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification.
imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 events
To mitigate the noisy nature of the Twitter stream, our system makes use of the COVID-Twitter-BERT (CT-BERT), which is a language model pre-trained on a large corpus of COVID-19 related Twitter messages.
Traffic Event Detection as a Slot Filling Problem
In particular, we experiment with several models to identify (i) whether a tweet is traffic-related or not, and (ii) in the case that the tweet is traffic-related to identify more fine-grained information regarding the event (e. g., the type of the event, where the event happened).
Learned Gradient Compression for Distributed Deep Learning
In contrast, we advocate that the gradients across the nodes are correlated and propose methods to leverage this inter-node redundancy to improve compression efficiency.
A Review on Fact Extraction and Verification
We study the fact checking problem, which aims to identify the veracity of a given claim.
Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks
Recent works use automatic extraction and ranking of candidate solution equations providing the answer to arithmetic word problems.
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations.
Zero-Shot Cross-Lingual Transfer with Meta Learning
We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.
Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A
This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task.
Sub-event detection from Twitter streams as a sequence labeling problem
This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level.
Adversarial training for multi-context joint entity and relation extraction
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data.
Ranked #7 on
Relation Extraction
on ACE 2004
Joint entity recognition and relation extraction as a multi-head selection problem
State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.
Ranked #7 on
Relation Extraction
on CoNLL04
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks.
Reconstructing the house from the ad: Structured prediction on real estate classifieds
In this paper, we address the (to the best of our knowledge) new problem of extracting a structured description of real estate properties from their natural language descriptions in classifieds.
