Character-level Intra Attention Network for Natural Language Inference

Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.Comment: EMNLP Workshop RepEval 2017: The Second Workshop on Evaluating Vector Space Representations for NL

Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts

Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.Peer ReviewedPostprint (published version

GRASS UTILIZATION IN GROWING FINISHING BÍSARO PIGS (85-107 KG). PERFORMANCE AND CARCASS COMPOSITION

The use of different quantities of vegetables, forages or fresh grass as fodder for growing-finishing pigs is an important factor of the northern Portugal traditional system. The increasing development of swine production in outdoor systems, extensive and organic production, turns to upcoming natural diets, in which grass performs a significant part. With regard to this, some investigation has been made concerning the use of fibre-rich feed ingredients in pig nutrition. Metabolic effects of its ingestion are analysed concerning different sights (economical, social, environmental and physiological ones). The aim of this work was to study the effects of grass utilization in the diets on performances of finishing Bísaro pigs. A total of 22 pigs (16 castrated males and 6 females) was housed outdoor and fed ad libitum (37 – 85 kg live weight) with a growing diet and then transferred to an indoor system (with free access to an outdoor area) for 49 days, according to 3 different treatments: 100% concentrate (C), 75% concentrate + ad libitum grass (CE75), 50% concentrate + ad libitum grass (CE50). The grass was supplied and its intake registered on a daily basis. Every 14 days, the pigs were weighted and their back fat (P2 in vivo) measured. After slaughter (average weight of 107 kg LW), yield and ½ left carcass characteristics were controlled. During the outdoor growing phase, the ADG was 513 g/day. During the indoor finishing phase, the increase grass intake was proportional to the reduction of concentrate in the diet. The ADG (g) and the fat deposition (P2 cm) were significantly different (P<0,05) in the 3 treatments (ADG: C=641, CE75=467, CE50=356 and: C=11,4, CE75=+9,5, CE50=+6,2). The empty body weight (kg) was also proportional to the intake of concentrate (C=116,2; CE75=107,7; CE50=102,2). Comparatively to the weight of the body parts, pigs that had higher intake of grass and lower of concentrate showed a higher % of shoulder (P<0,05; C=20,4, CE75=21,7, CE50=22,2) and the pH45min of CE carcasses was significantly higher (P<0,05). As a conclusion, concentrate substitution for grass showed a slower growing rate, thinner carcasses and a high technological quality. Neverthelles variability (CV %) of the productive parameters at the end of this study were higher in the treatments that included grass: live weight (C= 10,5%; C75=10,7%; C50=14,3%), finishing ADG (C=24%; C75=37%, C50=42%), and final fat (C=37%; C75=32%, C50=52%). These values suggest that the utilization of fibrous feeds in growing-finishing swine may be one of the possible explanations of the more heterogeneous products and carcasses found in the traditional or extensive systems, common users of fibrous feeds in the carcass finishing phase

UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, Gravity- Counts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the Frenchto- English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems.Postprint (published version

Using collocation segmentation to augment the phrase table

This paper describes the 2010 phrase-based statistical machine translation system developed at the TALP Research Center of the UPC1 in cooperation with BMIC2 and VMU3. In phrase-based SMT, the phrase table is the main tool in translation. It is created extracting phrases from an aligned parallel corpus and then computing translation model scores with them. Performing a collocation segmentation over the source and target corpus before the alignment causes that di erent and larger phrases are extracted from the same original documents. We performed this segmentation and used the union of this phrase set with the phrase set extracted from the nonsegmented corpus to compute the phrase table. We present the con gurations considered and also report results obtained with internal and o cial test sets.Postprint (published version

Plurality Voting: the statistical laws of democracy in Brazil

We explore the statistical laws behind the plurality voting system by investigating the election results for mayor held in Brazil in 2004. Our analysis indicate that the vote partition among mayor candidates of the same city tends to be "polarized" between two candidates, a phenomenon that can be closely described by means of a simple fragmentation model. Complex concepts like "government continuity" and "useful vote" can be identified and even statistically quantified through our approach.Comment: 4 pages, 4 figure