A Dynamic Approach to the Environmental Effects of Trade Liberalization

In this paper, we develop a two-country world di¤erential game model with a polluting firm in each country where there is transportation cost to investigate the equilibrium of the game between firms when they decide to trade or not and to see under which conditions social welfare coincides with the market equilibrium. We find out that in the static game bilateral trade is always the equilibrium for any acceptable transportation cost while in the dynamic game social planner can prevent the inefficient outcome by imposing and determining the proper amount of Pigouvian taxation.

Using Auctions for Pollution Rights as Indirect Incentives for Investments in Green Technologies

Acquired wisdom has it that the allocation of pollution rights to firms hinders their willingness to undertake uncertain R&D projects for environmental-friendly technologies. We revisit this issue in a model where firms strategically choose whether to participate in an auction to attain pollution permits, or instead invest in green R&D, to show that, somewhat counterintuitively, a side effect of the auction is in fact that of fostering environmental R&D in an admissible range of the model parameters.

Examining the Tip of the Iceberg: A Data Set for Idiom Translation

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains one of the open challenges in this field. Idioms, a category of multiword expressions, are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts. A first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation. In this paper we address this problem by creating the first large-scale data set for idiom translation. Our data set is automatically extracted from a widely used German-English translation corpus and includes, for each language direction, a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked. We release this data set and use it to perform preliminary NMT experiments as the first step towards better idiom translation.Comment: Accepted at LREC 201

Learning Topic-Sensitive Word Representations

Distributed word representations are widely used for modeling words in NLP tasks. Most of the existing models generate one representation per word and do not consider different meanings of a word. We present two approaches to learn multiple topic-sensitive representations per word by using Hierarchical Dirichlet Process. We observe that by modeling topics and integrating topic distributions for each document we obtain representations that are able to distinguish between different meanings of a given word. Our models yield statistically significant improvements for the lexical substitution task indicating that commonly used single word representations, even when combined with contextual information, are insufficient for this task.Comment: 5 pages, 1 figure, Accepted at ACL 201

Data Augmentation for Low-Resource Neural Machine Translation

The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.Comment: 5 pages, 2 figures, Accepted at ACL 201

A Dynamic Approach to the Environmental Effects of Trade Liberalization

In this paper, we develop a two-country world differential game model with a polluting firm in each country where there is transportation cost to investigate the equilibrium of the game between firms when they decide to trade or not and to see under which conditions social welfare coincides with the market equilibrium. We find out that in the static game bilateral trade is always the equilibrium for any acceptable transportation cost while in the dynamic game social planner can prevent the inefficient outcome by imposing and determining the proper amount of Pigouvian taxation

Understanding and Enhancing the Use of Context for Machine Translation

To understand and infer meaning in language, neural models have to learn complicated nuances. Discovering distinctive linguistic phenomena from data is not an easy task. For instance, lexical ambiguity is a fundamental feature of language which is challenging to learn. Even more prominently, inferring the meaning of rare and unseen lexical units is difficult with neural networks. Meaning is often determined from context. With context, languages allow meaning to be conveyed even when the specific words used are not known by the reader. To model this learning process, a system has to learn from a few instances in context and be able to generalize well to unseen cases. The learning process is hindered when training data is scarce for a task. Even with sufficient data, learning patterns for the long tail of the lexical distribution is challenging. In this thesis, we focus on understanding certain potentials of contexts in neural models and design augmentation models to benefit from them. We focus on machine translation as an important instance of the more general language understanding problem. To translate from a source language to a target language, a neural model has to understand the meaning of constituents in the provided context and generate constituents with the same meanings in the target language. This task accentuates the value of capturing nuances of language and the necessity of generalization from few observations. The main problem we study in this thesis is what neural machine translation models learn from data and how we can devise more focused contexts to enhance this learning. Looking more in-depth into the role of context and the impact of data on learning models is essential to advance the NLP field. Moreover, it helps highlight the vulnerabilities of current neural networks and provides insights into designing more robust models.Comment: PhD dissertation defended on November 10th, 202

Polyester resin concrete: mix development and the structural behaviour of some components

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