Readability is a fundamental problem in textbooks assessment.For low resources languages(LRL),however,little investigation has been done on the readability of textbook.In this paper,we proposed a readability assessmen...Readability is a fundamental problem in textbooks assessment.For low resources languages(LRL),however,little investigation has been done on the readability of textbook.In this paper,we proposed a readability assessment method for Tibetan textbook(a low resource language).We extract features based on the information that are gotten by Tibetan segmentation and named entity recognition.Then,we calculate the correlation of different features using Pearson Correlation Coefficient and select some feature sets to design the readability formula.Fit detection,F test and T test are applied on these selected features to generate a new readability assessment formula.Experiment shows that this new formula is capable of assessing the readability of Tibetan textbooks.展开更多
The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a ver...The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a very difficult task especially for low resource languages such as Chinese-Vietnamese. Fortunately,multilingual user generated contents( UGC),such as bilingual movie subtitles,provide us access to automatic construction of the parallel corpus. Although the amount of UGC parallel corpora can be considerable,the original corpus is not suitable for statistical machine translation( SMT) systems. The corpus may contain translation errors,sentence mismatching,free translations,etc. To improve the quality of the bilingual corpus for SMT systems,three filtering methods are proposed: sentence length difference,the semantic of sentence pairs,and machine learning. Experiments are conducted on the Chinese to Vietnamese translation corpus.Experimental results demonstrate that all the three methods effectively improve the corpus quality,and the machine translation performance( BLEU score) can be improved by 1. 32.展开更多
Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile l...Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile language resource,particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge.Despite its potential,a detailed set of baselines has not yet been developed for Pirá.By creating these baselines,researchers can more easily utilize Piráas a resource for testing machine learning models across a wide range of question answering tasks.In this paper,we define six benchmarks over the Pirádataset,covering closed generative question answering,machine reading comprehension,information retrieval,open question answering,answer triggering,and multiple choice question answering.As part of this effort,we have also produced a curated version of the original dataset,where we fixed a number of grammar issues,repetitions,and other shortcomings.Furthermore,the dataset has been extended in several new directions,so as to face the aforementioned benchmarks:translation of supporting texts from English into Portuguese,classification labels for answerability,automatic paraphrases of questions and answers,and multiple choice candidates.The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pirádataset.展开更多
基金This work was supported by the China National Natural Science Foundation No.(61331013)the Young faculty scientific research ability promotion program of Minzu University of China.
文摘Readability is a fundamental problem in textbooks assessment.For low resources languages(LRL),however,little investigation has been done on the readability of textbook.In this paper,we proposed a readability assessment method for Tibetan textbook(a low resource language).We extract features based on the information that are gotten by Tibetan segmentation and named entity recognition.Then,we calculate the correlation of different features using Pearson Correlation Coefficient and select some feature sets to design the readability formula.Fit detection,F test and T test are applied on these selected features to generate a new readability assessment formula.Experiment shows that this new formula is capable of assessing the readability of Tibetan textbooks.
基金Supported by the National Basic Research Program of China(973Program)(2013CB329303)the National Natural Science Foundation of China(61502035)
文摘The performance of a machine translation system heavily depends on the quantity and quality of the bilingual language resource. However,getting a parallel corpus,which has a large scale and is of high quality,is a very difficult task especially for low resource languages such as Chinese-Vietnamese. Fortunately,multilingual user generated contents( UGC),such as bilingual movie subtitles,provide us access to automatic construction of the parallel corpus. Although the amount of UGC parallel corpora can be considerable,the original corpus is not suitable for statistical machine translation( SMT) systems. The corpus may contain translation errors,sentence mismatching,free translations,etc. To improve the quality of the bilingual corpus for SMT systems,three filtering methods are proposed: sentence length difference,the semantic of sentence pairs,and machine learning. Experiments are conducted on the Chinese to Vietnamese translation corpus.Experimental results demonstrate that all the three methods effectively improve the corpus quality,and the machine translation performance( BLEU score) can be improved by 1. 32.
基金The work was carried out at the Center for Artificial Intelligence(C4AI-USP)with support from the São Paulo Research Foundation(FAPESP grant#2019/07665-4)from the IBM Corporation.This research was also partially supported by ItaúUnibanco S.A.+1 种基金M.M.Joséand F.Nakasato have been supported by the ItaúScholarship Program(PBI)of the Data Science Center(C2D)of the Escola Politécnica da Universidade de São PauloWe acknowledge support by CAPES-Finance Code 001.A.H.R.Costa and F.G.Cozman were partially supported by CNPq grants 310085/2020-9 and 305753/2022-3 respectively.Paulo Pirozelli was supported by the FAPESP grant 2019/26762-0.
文摘Piráis a reading comprehension dataset focused on the ocean,the Brazilian coast,and climate change,built from a collection of scientific abstracts and reports on these topics.This dataset represents a versatile language resource,particularly useful for testing the ability of current machine learning models to acquire expert scientific knowledge.Despite its potential,a detailed set of baselines has not yet been developed for Pirá.By creating these baselines,researchers can more easily utilize Piráas a resource for testing machine learning models across a wide range of question answering tasks.In this paper,we define six benchmarks over the Pirádataset,covering closed generative question answering,machine reading comprehension,information retrieval,open question answering,answer triggering,and multiple choice question answering.As part of this effort,we have also produced a curated version of the original dataset,where we fixed a number of grammar issues,repetitions,and other shortcomings.Furthermore,the dataset has been extended in several new directions,so as to face the aforementioned benchmarks:translation of supporting texts from English into Portuguese,classification labels for answerability,automatic paraphrases of questions and answers,and multiple choice candidates.The results described in this paper provide several points of reference for researchers interested in exploring the challenges provided by the Pirádataset.