Automated Fact Checking in the News Room

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact-checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user-friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58\% of the time, and 59\% of the returned evidence was relevant

Scaling Recurrent Neural Network Language Models

This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than n-gram models. We train the largest known RNNs and present relative word error rates gains of 18% on an ASR task. We also present the new lowest perplexities on the recently released billion word language modelling benchmark, 1 BLEU point gain on machine translation and a 17% relative hit rate gain in word prediction

Compressing Word Embeddings

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic. However, these vector space representations (created through large-scale text analysis) are typically stored verbatim, since their internal structure is opaque. Using word-analogy tests to monitor the level of detail stored in compressed re-representations of the same vector space, the trade-offs between the reduction in memory usage and expressiveness are investigated. A simple scheme is outlined that can reduce the memory footprint of a state-of-the-art embedding by a factor of 10, with only minimal impact on performance. Then, using the same `bit budget', a binary (approximate) factorisation of the same space is also explored, with the aim of creating an equivalent representation with better interpretability.Comment: 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental results were submitted to ICLR-2016, but the paper has been significantly updated, since a new experimental set-up worked much bette

Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation

The neural language models (NLM) achieve strong generalization capability by learning the dense representation of words and using them to estimate probability distribution function. However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates. To address this problem, we propose a method to enrich representations of rare words in pre-trained NLM and consequently improve its probability estimation performance. The proposed method augments the word embedding matrices of pre-trained NLM while keeping other parameters unchanged. Specifically, our method updates the embedding vectors of rare words using embedding vectors of other semantically and syntactically similar words. To evaluate the proposed method, we enrich the rare street names in the pre-trained NLM and use it to rescore 100-best hypotheses output from the Singapore English speech recognition system. The enriched NLM reduces the word error rate by 6% relative and improves the recognition accuracy of the rare words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201