Lattice score based data cleaning for phrase-based statistical machine translation

Statistical machine translation relies heavily on parallel corpora to train its models for translation tasks. While more and more bilingual corpora are readily available, the quality of the sentence pairs should be taken into consideration. This paper presents a novel lattice score-based data cleaning method to select proper sentence pairs from the ones extracted from a bilingual corpus by the sentence alignment methods. The proposed method is carried out as follows: firstly, an initial phrasebased model is trained on the full sentencealigned corpus; then for each of the sentence pairs in the corpus, word alignments are used to create anchor pairs and sourceside lattices; thirdly, based on the translation model, target-side phrase networks are expanded on the lattices and Viterbi searching is used to find approximated decoding results; finally, BLEU score thresholds are used to filter out the low-score sentence pairs for the data cleaning purpose. Our experiments on the FBIS corpus showed improvements of BLEU score from 23.78 to 24.02 in Chinese-English

A plea for more interactions between psycholinguistics and natural language processing research

A new development in psycholinguistics is the use of regression analyses on tens of thousands of words, known as the megastudy approach. This development has led to the collection of processing times and subjective ratings (of age of acquisition, concreteness, valence, and arousal) for most of the existing words in English and Dutch. In addition, a crowdsourcing study in the Dutch language has resulted in information about how well 52,000 lemmas are known. This information is likely to be of interest to NLP researchers and computational linguists. At the same time, large-scale measures of word characteristics developed in the latter traditions are likely to be pivotal in bringing the megastudy approach to the next level

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems