Named Entity Recognition in multilingual handwritten texts

[ES] En nuestro trabajo presentamos un único modelo basado en aprendizaje profundo para la transcripción automática y el reconocimiento de entidades nombradas de textos manuscritos. Este modelo aprovecha las capacidades de generalización de sistemas de reconocimiento, combinando redes neuronales artificiales y n-gramas de caracteres. Se discute la evaluación de dicho sistema y, como consecuencia, se propone una nueva medida de evaluación. Con el fin de mejorar los resultados con respecto a dicha métrica, se evalúan diferentes estrategias de corrección de errores.[EN] In our work we present a single Deep Learning based model for the automatic transcription and Named Entity Recognition of handwritten texts. Such model leverages the generalization capabilities of recognition systems, combining Artificial Neural Networks and n-gram character models. The evaluation of said system is discussed and, as a consequence, a new evaluation metric is proposed. As a means to improve the results in regards to such metric, different error correction strategies are assessed.Villanova Aparisi, D. (2021). Named Entity Recognition in multilingual handwritten texts. Universitat Politècnica de València. http://hdl.handle.net/10251/174942TFG

Neural probabilistic language model for system combination

This paper gives the system description of the neural probabilistic language modeling (NPLM) team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). We used the information obtained by NPLM as meta information to the system combination module. For the Spanish-English data, our paraphrasing approach achieved 25.81 BLEU points, which lost 0.19 BLEU points absolute compared to the standard confusion network-based system combination. We note that our current usage of NPLM is very limited due to the difficulty in combining NPLM and system combination

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an input video to a variable-length output sentence in a sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless, the training of RNN still suffers to some degree from vanishing/exploding gradient problem, making the optimization difficult. Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations. In this paper, we present a novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning. Technically, we exploit convolutional block structures that compute intermediate states of a fixed number of inputs and stack several blocks to capture long-term relationships. The structure in encoder is further equipped with temporal deformable convolution to enable free-form deformation of temporal sampling. Our model also capitalizes on temporal attention mechanism for sentence generation. Extensive experiments are conducted on both MSVD and MSR-VTT video captioning datasets, and superior results are reported when comparing to conventional RNN-based encoder-decoder techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8% to 67.2% on MSVD.Comment: AAAI 201

Influence of Sequential and Simultaneous Bilingualism on Second Grade Dual Language Students’ Use of Syntax in Reading

This study integrated reading and bilingual theories to examine the reading behaviors of second grade Latinx students in a Dual Language program. It explored how the students’ differing language backgrounds (simultaneous bilinguals and sequential bilinguals who had developed Spanish as a home language) might influence their early reading behaviors. Data sources include Spanish and English informal reading inventories and student interviews. While all students struggled with the syntax of grade-level text in Spanish and English, simultaneous bilinguals’ superior ability to use syntax to self-correct in both languages evidenced a stronger syntactical base. The findings have significant implications for equity of instruction and assessment, including: the need to find new ways to analyze bilingual students’ informal reading inventories from an assets-oriented lens, prudence regarding how informal reading inventory results are used, the need for intensive language development in schools, and the use of caution when applying translanguaging theory to emergent bilingual readers