Word Alignment Model Research Articles

Exploration and Research on Chinese Culture in University English Translation Teaching in the Background of Internet+

Abstract Under the background of Internet+, for the problems of high comprehension difficulty and inaccurate translation in university English translation teaching, this paper proposes the skeleton knowledge translation model to assist university English translation teaching that integrates Chinese culture. Starting from the objectives of university English translation teaching, Chinese culture is explored in university English translation materials to complete the design of university English translation teaching that integrates Chinese culture. Propose the application of Internet technology in the above teaching design, utilize the IBM word alignment model and TF-IDF similarity to preprocess the university English translation textbooks, and on the basis of preprocessing, estimate the probability and decode the generated phrase pairs, and then complete the construction of the English translation model based on skeleton knowledge. The English translation model’s performance analysis is carried out using skeleton knowledge after determining the data set and evaluation criteria. The results show that for the whole 400 000 batch training, the training time of the English translation model based on skeleton knowledge is about one-fifth less than that of the unsupervised neural machine translation model, indicating that with the increase of the number of rounds, the performance of the English translation model based on skeleton knowledge is also better. This study serves as a guide for exploring and researching Chinese culture in university English translation teaching and promotes the development of university English translation teaching in colleges and universities.

Application of the Neural Network Based on the Multilayer Perceptron Genetic Algorithm in Chinese-English Two-Way Translation

With the increasing application of the perceptron genetic algorithm neural network in Chinese-English two-way translation, there are many translation problems to be solved. In order to solve the translation problem of Chinese-English parallel corpus, the multilayer perceptron method, genetic word alignment model (GA), language model, and neural network method (including the translation model and bilingual pretraining model) are designed, which are combined into the ga-mlp-nn combination model to measure the parallelism of Chinese and English sentences from different emphases. The results show that the ga-mlp-nn model has good performance in filtering high-quality parallel corpus. The final experimental results show that compared with a single system, the improved multisystem fusion method based on weight multiplication has achieved better results in the test set. In the last five groups of evaluation results, the system submitted in this paper ranks second and first in multiple datasets, which has a certain reference value for the research of corpus filtering.

Opinion Relation Co-Extraction Based on Partially-Supervised Topical Relations Word Alignment Model

The most critical tools for fine-grained opinion extraction are opinion goals and opinion terms extracted from on-line comments. The key part of this process is to identify the connection between terms. To do this, the Word Alignment Model (WAM) was introduced in which the associated variable can be identified by word alignment by an opinion goal. Nevertheless, its ability to extract opinion words was less successful. In order to determine opinion connections as a process of alignment, the partially supervised Word Alienation Model (PSWAM) has therefore been created. Then a visual co-ranking algorithm was implemented together with the Opinion Relationship Map, to model all the candidates and to measure the confidence of each voter by defining their opinion. In addition, higher-confidence candidates were extracted as opinions or opinions. This method, though, involves an added kind of interaction with terms such as topical connections in graphic thought. Therefore the current relationship is assumed in this report in order to model the applicants and derive the feelings, views and opinions. The efficiency of co-extracting thoughts, viewpoints and issues is enhanced effectively by using this method. The experimental results further indicate that compared to the existing paradigm, the efficiency of the proposed model.

Word.alignment: an R package for computing statistical word alignment and its evaluation

Word alignment has lots of applications in various natural language processing (NLP) tasks. As far as we are aware, there is no word alignment package in the R environment. In this paper, word.alignment, a new R software package is introduced which implements a statistical word alignment model as an unsupervised learning. It uses IBM Model 1 as a machine translation model based on the use of the EM algorithm and the Viterbi search in order to find the best alignment. It also provides the symmetric alignment using three heuristic methods such as union, intersection, and grow-diag. It has also the ability to build an automatic bilingual dictionary applying an innovative rule. The generated dictionary is suitable for a number of NLP tasks. This package provides functions for measuring the quality of the word alignment via comparing the alignment with a gold standard alignment based on five metrics as well. It is easily installed and executable on the mostly widely used platforms. Note that it is easily usable and we show that its results are almost everywhere better than some other word alignment tools. Finally, some examples illustrating the use of word.alignment is provided.

Research on statistical machine translation model based on deep neural network

With the increase of translation demand, the advancement of information technology, the development of linguistic theories and the progress of natural language understanding models in artificial intelligence research, machine translation has gradually gained worldwide attention. However, at present, machine translation research still has problems such as insufficient bilingual data and lack of effective feature representation, which affects the further improvement of key modules of machine translation such as word alignment, sequence adjustment and translation modelling. The effect of machine translation is still unsatisfactory. As a new machine learning method, deep neural network can automatically learn abstract feature representation and establish a complex mapping relationship between input and output signals, which provides a new idea for statistical machine translation research. Firstly, the multi-layer neural network and the undirected probability graph model are combined, and the similarity and context information of vocabulary are effectively utilized to model the word alignment more fully, and the word alignment model named NNWAM is constructed. Secondly, the low dimension will be used. The feature representation is combined with other features into a linearly ordered pre-ordering model to construct the pre-ordering model named NNPR. Finally, the word alignment model and the pre-ordering model are combined in the same deep neural network framework to form DNNAPM, a statistical machine translation model based on deep neural networks. The experimental results show that the statistical machine translation model based on deep neural network has better effect, faster convergence and better reliability than the comparison model algorithm.

Induction of latent domains in heterogeneous corpora: a case study of word alignment

This paper focuses on the insensitivity of existing word alignment models to domain differences, which often yields suboptimal results on large heterogeneous data. A novel latent domain word alignment model is proposed, which induces domain-focused lexical and alignment statistics. We propose to train the model on a heterogeneous corpus under partial supervision, using a small number of seed samples from different domains. The seed samples allow estimating sharper, domain-focused word alignment statistics for sentence pairs. Our experiments show that the derived domain-focused statistics, once combined together, produce significant improvements both in word alignment accuracy and in translation accuracy of their resulting SMT systems. Going beyond the findings, we surmise that virtually any large corpus (e.g., Europarl, Hansards, Common Crawl) harbors an arbitrary diversity of hidden domains, unknown in advance. We address the novel challenge of unsupervised induction of hidden domains in parallel corpora, applied within a domain-focused word-alignment modeling framework. On the technical side, we contrast flat estimation for the unsupervised induction of domains to a simple form of hierarchical estimation, consisting of two steps aiming at avoiding bad local maxima. Extensive experiments, conducted over seven different language pairs with fully unsupervised induction of domains for word alignment, demonstrate significant improvements in alignment accuracy.

Linking Opinions to Products: A Word Alignment Approach to Opinion Mining

With the rapid development of e-commerce, most customers express their opinions in the internet. The number of reviews can be in hundreds or even thousands. This makes the customers to read all the reviews. A novel approach proposed to perform aspect based opinion mining in online product reviews. We are using word alignment model with rule based approach (Part-Of-Speech rules and syntactic rules) to identify opinion relations (relationships and association between opinion words and its opinion targets) as an alignment process. Then each candidate’s confidence is estimated through a graph-based co-ranking algorithm. Candidates with higher confidence are extracted as opinion targets or opinion words. Naive Bayes algorithm is used to identify the polarity the opinion words into positive, negative and neutral categories.

Linguistic-Relationships-Based Approach for Improving Word Alignment

The unsupervised word alignments (such as GIZA++) are widely used in the phrase-based statistical machine translation. The quality of the model is proportional to the size and the quality of the bilingual corpus. However, for low-resource language pairs such as Chinese and Vietnamese, a result of unsupervised word alignment sometimes is of low quality due to the sparse data. In addition, this model does not take advantage of the linguistic relationships to improve performance of word alignment. Chinese and Vietnamese have the same language type and have close linguistic relationships. In this article, we integrate the characteristics of linguistic relationships into the word alignment model to enhance the quality of Chinese-Vietnamese word alignment. These linguistic relationships are Sino-Vietnamese and content word. The experimental results showed that our method improved the performance of word alignment as well as the quality of machine translation.

Research on Deep Learning HMM Word Alignment

Word alignment is an essential step for machine translation. According to the over-fitting of traditional word alignment model and the weakness for the context description, the deep learning hidden HMM word alignment combined a multi-layer neural network with an undirected probabilistic graph model, use the similarity of the word and context information word alignment to be a more precise modeling. Experimental results show that, compared with the reference system, this model can significantly improve the effect of word alignment, which is applicable.

Online Reviews Based on the Word Alignment Model

Online Reviews Based on the Word Alignment Model

English

English

Multilingual Topic Models for Bilingual Dictionary Extraction

A machine-readable bilingual dictionary plays a crucial role in many natural language processing tasks, such as statistical machine translation and cross-language information retrieval. In this article, we propose a framework for extracting a bilingual dictionary from comparable corpora by exploiting a novel combination of topic modeling and word aligners such as the IBM models. Using a multilingual topic model, we first convert a comparable document -aligned corpus into a parallel topic -aligned corpus. This novel topic-aligned corpus is similar in structure to the sentence -aligned corpus frequently employed in statistical machine translation and allows us to extract a bilingual dictionary using a word alignment model. The main advantages of our framework is that (1) no seed dictionary is necessary for bootstrapping the process, and (2) multilingual comparable corpora in more than two languages can also be exploited. In our experiments on a large-scale Wikipedia dataset, we demonstrate that our approach can extract higher precision dictionaries compared to previous approaches and that our method improves further as we add more languages to the dataset.

Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model

Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations. Compared to syntax-based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state-of-the-art methods.

Maximum-entropy word alignment and posterior-based phrase extraction for machine translation

One of the fundamental assumptions in statistical machine translation (SMT) is that the correspondence between a sentence and its translation can be explained in terms of an alignment between their words. Such alignment information is typically not observed in the parallel corpora used to build the phrase table of an SMT system. Therefore, it is customary to estimate a probabilistic model of the assumed hidden word alignment, which is then used to extract bilingual phrase pairs. In standard extraction heuristics, the alignment model is under-exploited as the only information used from the posterior distribution is the Viterbi best alignment. This is due to the high computational complexity of the IBM models, which are the de facto standard for computing these alignments. Note that these models have other limitations, including their asymmetry and their inability to integrate rich, feature-based, descriptions. We argue that refining the word alignment model in a discriminative maximum-entropy framework substantially improves the alignment quality. We also show that these improved alignments combined with efficient and accurate computation of the link posterior distributions can also improve the overall translation performance, especially when applying posterior-based extraction methods.

Unsupervised Query Segmentation Using Monolingual Word Alignment Method

In this paper, we propose a novel unsupervised approach to query segmentation using the word alignment model which is usually adopted in statistical machine translation system. Query segmentation is to obtain complete phrases or concepts in a query by segmenting a sequence of query terms, which is an important query processing procedure for improving information retrieval performance in search engines. In this work, we use a novel monolingual word alignment method to segment queries and automatically obtain the query structure in the form of multilevel segmentation. Our approach is language independent and unsupervised so that it is easy to be applied to various language scenarios. Experimental results on a real-world query dataset show that our approach outperforms the state of the art language model based method, which demonstrates the effectiveness of the proposed approach in query segmentation.

Constraining a Generative Word Alignment Model with Discriminative Output

We present a method to constrain a statistical generative word alignment model with the output from a discriminative model. The discriminative model is trained using a small set of hand-aligned data that ensures higher precision in alignment. On the other hand, the generative model improves the recall of alignment. By combining these two models, the alignment output becomes more suitable for use in developing a translation model for a phrase-based statistical machine translation (SMT) system. Our experimental results show that the joint alignment model improves the translation performance. The improvement in average of BLEU and METEOR scores is around 1.0-3.9 points.

A statistical approach to crosslingual natural language tasks

The existence of huge volumes of documents written in multiple languages on Internet leads to investigate novel algorithmic approaches to deal with information of this kind. However, most crosslingual natural language processing (NLP) tasks consider a decoupled approach in which monolingual NLP techniques are applied along with an independent translation process. This two-step approach is too sensitive to translation errors, and in general to the accumulative effect of errors. To solve this problem, we propose to use a direct probabilistic crosslingual NLP system which integrates both steps, translation and the specific NLP task, into a single one. In order to perform this integrated approach to crosslingual tasks, we propose to use the statistical IBM 1 word alignment model (M1). The M1 model may show a non-monotonic behaviour when aligning words from a sentence in a source language to words from another sentence in a different, target language. This is the case of languages with different word order. In English, for instance, adjectives appear before nouns, whereas in Spanish it is exactly the opposite. The successful experimental results reported in three different tasks – text classification, information retrieval and plagiarism analysis – highlight the benefits of the statistical integrated approach proposed in this work.

A word alignment model based on multiobjective evolutionary algorithms

Word alignment is a key task in statistical machine translation (SMT). This paper presents a novel model for this task. In this model, word alignment is considered as a multiobjective optimization problem and solved based on the non-dominated sorting genetic algorithm II (NSGA-II), which is one of the best multiobjective evolutionary algorithms (MOEA). There are two advantages of the proposed model based on NSGA-II. First, it could be easily extended through incorporating new objective functions. Secondly, it does not need any hand-aligned word-level alignment data to determine the weight of each objective function. Experiments were carried out and the results show that the proposed model outperforms the IBM translation models significantly.

Parse tree-based machine translation for less-used languages

The article describes a method that enhances translation performance of language pairs with a less used source language and a widely used target language. We propose a method that enables the use of parse tree based statistical translation algorithms for language pairs with a less used source language and a widely used target language. Automatic part of speech (POS) tagging algorithms have become accurate to the extent of efficient use in many tasks. Most of these methods are quite easily implementable in most world languages. The method is divided in two parts; the first part constructs alignments between POS tags of source sentences and induced parse trees of target language. The second part searches through trained data and selects the best candidates for target sentences, the translations. The method was not fully implemented due to time constraints; the training part was implemented and incorporated into a functional translation system; the inclusion of a word alignment model into the translation part was not implemented. The empirical evaluation addressing the quality of trained data was carried out on a full implementation of the presented training algorithms and the results confirm the employability of the method.

A word-to-phrase statistical translation model

This article addresses the development of statistical models for phrase-based machine translation (MT) which extend a popular word-alignment model proposed by IBM in the early 90s. A novel decoding algorithm is directly derived from the optimization criterion which defines the statistical MT approach. Efficiency in decoding is achieved by applying dynamic programming, pruning strategies, and word reordering constraints. It is known that translation performance can be boosted by exploiting phrase (or multiword) translation pairs automatically extracted from a parallel corpus. New phrase-based models are obtained by introducing extra multiwords in the target language vocabulary and by estimating the corresponding parameters from either: (i) a word-based model, (ii) phrase-based statistics computed on the parallel corpus, or (iii) the interpolation of the two previous estimates. Word-based and phrase-based MT models are evaluated on a traveling domain task in two translation directions: Chinese-English (12k-word vocabulary) and Italian-English (16k-word vocabulary). Phrase-based models show Bleu score improvements over the word-based model by 19% and 13% relative, respectively.