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

Constrained word alignment models for statistical machine translation

Word alignment is a fundamental and crucial component in Statistical Machine Translation (SMT) systems. Despite the enormous progress made in the past two decades, this task remains an active research topic simply because the quality of word alignment is still far from optimal. Most state-of-the-art word alignment models are grounded on statistical learning theory treating word alignment as a general sequence alignment problem, where many linguistically motivated insights are not incorporated. In this thesis, we propose new word alignment models with linguistically motivated constraints in a bid to improve the quality of word alignment for Phrase-Based SMT systems (PB-SMT). We start the exploration with an investigation into segmentation constraints for word alignment by proposing a novel algorithm, namely word packing, which is motivated by the fact that one concept expressed by one word in one language can frequently surface as a compound or collocation in another language. Our algorithm takes advantage of the interaction between segmentation and alignment, starting with some segmentation for both the source and target language and updating the segmentation with respect to the word alignment results using state-of-the-art word alignment models; thereafter a refined word alignment can be obtained based on the updated segmentation. In this process, the updated segmentation acts as a hard constraint on the word alignment models and reduces the complexity of the alignment models by generating more 1-to-1 correspondences through word packing. Experimental results show that this algorithm can lead to statistically significant improvements over the state-of-the-art word alignment models. Given that word packing imposes "hard" segmentation constraints on the word aligner, which is prone to introducing noise, we propose two new word alignment models using syntactic dependencies as soft constraints. The first model is a syntactically enhanced discriminative word alignment model, where we use a set of feature functions to express the syntactic dependency information encoded in both source and target languages. One the one hand, this model enjoys great flexibility in its capacity to incorporate multiple features; on the other hand, this model is designed to facilitate model tuning for different objective functions. Experimental results show that using syntactic constraints can improve the performance of the discriminative word alignment model, which also leads to better PB-SMT performance compared to using state-of-the-art word alignment models. The second model is a syntactically constrained generative word alignment model, where we add in a syntactic coherence model over the target phrases in the context of HMM word-to-phrase alignment. The advantages of our model are that (i) the addition of the syntactic coherence model preserves the efficient parameter estimation procedures; and (ii) the flexibility of the model can be increased so that it can be tuned according to different objective functions. Experimental results show that tuning this model properly leads to a significant gain in MT performance over the state-of-the-art

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

On the Translation Strategies of Human-computer Interaction Based on Machine Translation

With the rapid development of language service industry and artificial intelligence technology, machine translation plays a more prominent role in the translation industry. Human-computer interaction translation greatly improves the speed and quality of translation, and pre-translation editing and post-translation editing are two important links and manifestations in human-computer interaction collaborative translation. On the basis of summarizing machine translation problems, this paper proposes translation strategies including replacing well-translated terms in advance, rewriting, addition, omission, and shift via pre-editing and post-editing, which greatly improves the quality of machine translation

Exploiting shared Chinese characters in Chinese word segmentation optimization for Chinese-Japanese machine translation

Abstract Unknown words and word segmentation granularity are two main problems in Chinese word segmentation for ChineseJapanese Machine Translation (MT). In this paper, we propose an approach of exploiting common Chinese characters shared between Chinese and Japanese in Chinese word segmentation optimization for MT aiming to solve these problems. We augment the system dictionary of a Chinese segmenter by extracting Chinese lexicons from a parallel training corpus. In addition, we adjust the granularity of the training data for the Chinese segmenter to that of Japanese. Experimental results of Chinese-Japanese MT on a phrase-based SMT system show that our approach improves MT performance significantly

SEA_AP: una herramienta de segmentación y etiquetado para el análisis prosódico

This paper introduces a tool that performs segmentation and labelling of sound chains in phono units, syllables and/or words departing from a sound signal and its corresponding orthographic transcription. In addition, it also integrates acoustic analysis scripts applied to the Praat programme with the aim of reducing the time spent on tasks related to analysis, correction, smoothing and generation of graphics of the melodic curve. The tool is implemented for Galician, Spanish and Brazilian Portuguese. Our goal is to contribute, by means of this application, to automatize some of the tasks of segmentation, labelling and prosodic analysis, since these tasks require a large investment of time and human resources.En este artículo se presenta una herramienta que realiza la segmentación y el etiquetado de cadenas sonoras en unidades de fono, sílaba y/o palabra partiendo de una señal sonora y de su correspondiente transcripción ortográfica. Además, integra scripts de análisis acústico que se ejecutan sobre el programa Praat con el fin de reducir el tiempo invertido en las tareas de análisis, corrección, suavizado y generación de gráficos de la curva melódica. La herramienta está implementada para gallego, español y portugués de Brasil. Nuestro objetivo es contribuir con esta aplicación a automatizar algunas de las labores de segmentación, etiquetado y análisis prosódico, pues constituyen tareas que requieren una gran inversión de tiempo y de recursos humanos.This work would have not been possible without the help of the Spanish Government (Project ‘SpeechTech4All’ TEC2012-38939-C03-01), the European Regional Development Fund (ERDF), the Government of the Autonomous Community of Galicia (GRC2014/024, “Consolidación de Unidades de Investigación: Proyecto AtlantTIC” CN2012/160) and the “Red de Investigación TecAnDAli” from the Council of Culture, Education and University Planning, Xunta de GaliciaS