Previous work has modeled the compositionality of words by creating characterlevel models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.
The renegotiation of a trade agreement introduces uncertainty into the economic environment. We exploit the natural experiment of the Brexit referendum to estimate the impact of uncertainty associated with trade agreement renegotiation. Empirically, we develop measures of the trade policy uncertainty facing firms exporting from the UK to the EU after June 2016. Using the universe of UK export transactions at the firm and product level, we estimate entry (exit) in 2016 would have been 5.0% higher (6.1% lower) if firms exporting from the UK to the EU had not faced increased trade policy uncertainty after June 2016.* This work contains statistical data from HMRC which is Crown Copyright. The research datasets used may not exactly reproduce HMRC aggregates. The use of HMRC statistical data in this work does not imply the endorsement of HMRC in relation to the interpretation or analysis of the information.
Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs. 1
A simple, efficient and highly functional group compatible method for the synthesis of propargylamines from terminal alkynes, dichloromethane and tertiary amines using silver catalysts has been developed.
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