Publications

Refine Results

(Filters Applied) Clear All

By

Using K-means in SVR-based text difficulty estimation

September 20, 2019
  |
Conference Paper
Author:
Raymond H. Budd
…
Published in:
8th ISCA Workshop on Speech and Language Technology in Education, SLaTE, 20-21 September 2019.
Topic:
human language technology
R&D area:
R&D group:

Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for learners in 18 languages. The Auto-ILR subscription service scans web feeds, extracts article content, evaluates the difficulty, and notifies users of documents that match their skill level. Difficulty is measured on the standard ILR scale with language-specific support vector machine regression (SVR) models built from vectors incorporating length features, term frequencies, relative entropy, and K-means clustering.
READ LESS

Summary

A challenge for second language learners, educators, and test creators is the identification of authentic materials at the right level of difficulty. In this work, we present an approach to automatically measure text difficulty, integrated into Auto-ILR, a web-based system that helps find text material at the right level for...

READ MORE
Using K-means in SVR-based text difficulty estimation

NetProf iOS pronunciation feedback demonstration

December 13, 2015
  |
Conference Paper
Author:
Tamas Marius
…
Published in:
IEEE Automatic Speech Recognition and Understanding Workshop, ASRU, 13 December 2015.
Topic:
speech recognition
R&D area:
R&D group:

Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are not widely studied in the U.S. Many students struggle to attain speaking fluency and proper pronunciation. Teaching pronunciation is a time-intensive task for teachers that requires them to give individual feedback to students during classroom hours. This limits the time teachers can spend imparting other information, and students may feel embarrassed or inhibited when they practice with their classmates. Given the demand for students educated in foreign languages and the limited number of qualified teachers in languages of interest, there is a growing need for computer-based tools students can use to practice and receive feedback at their own pace and schedule. Most existing tools are limited to listening to pre-recorded audio with limited or nonexistent support for pronunciation feedback. MIT Lincoln Laboratory has developed a new tool, Net Pronunciation Feedback (NetProF), to address these challenges and improve student pronunciation and general language fluency.
READ LESS

Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are...

READ MORE
NetProf iOS pronunciation feedback demonstration

A new multiple choice comprehension test for MT

May 26, 2014
  |
Conference Paper
Author:
Douglas A. Jones
…
Published in:
Automatic and Manual Metrics for Operation Translation Evaluation Workshop, 9th Int. Conf. on Language Resources and Evaluation (LREC 2014), 26 May 2014.
Topic:
human language technology
R&D area:
R&D group:

Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic documents into English from two different MT systems. We created two forms of the test: Form A has the original English documents and output from the two Arabic-to-English MT systems. Form B has English, Arabic, and one of the MT system outputs. We administered the comprehension test to three subject types recruited in the greater Boston area: (1) native English speakers with no Arabic skills, (2) Arabic language learners, and (3) Native Arabic speakers who also have English language skills. There were 36 native English speakers, 13 Arabic learners, and 11 native Arabic speakers with English skills. Subjects needed an average of 3.8 hours to complete the test, which had 191 questions and 59 documents. Native English speakers with no Arabic skills saw Form A. Arabic learners and native Arabic speakers saw form B.
READ LESS

Summary

We present results from a new machine translation comprehension test, similar to those developed in previous work (Jones et al., 2007). This test has documents in four conditions: (1) original English documents; (2) human translations of the documents into Arabic; conditions (3) and (4) are machine translations of the Arabic...

READ MORE
A new multiple choice comprehension test for MT

Standardized ILR-based and task-based speech-to-speech MT evaluation

May 26, 2014
  |
Conference Paper
Author:
Douglas A. Jones
…
Published in:
Automatic and Manual Metrics for Operation Translation Evaluation Workshop, 9th Int. Conf. on Language Resources and Evaluation (LREC 2014), 26 May 2014.
Topic:
human language technology
R&D area:
R&D group:

Summary

This paper describes a new method for task-based speech-to-speech machine translation evaluation, in which tasks are defined and assessed according to independent published standards, both for the military tasks performed and for the foreign language skill levels used. We analyze task success rates and automatic MT evaluation scores (BLEU and METEOR) for 220 role-play dialogs. Each role-play team consisted of one native English-speaking soldier role player, one native Pashto-speaking local national role player, and one Pashto/English interpreter. The overall PASS score, averaged over all of the MT dialogs, was 44%. The average PASS rate for HT was 95%, which is important because a PASS requires that the role-players know the tasks. Without a high PASS rate in the HT condition, we could not be sure that the MT condition was not being unfairly penalized. We learned that success rates depended as much on task simplicity as it did upon the translation condition: 67% of simple, base-case scenarios were successfully completed using MT, whereas only 35% of contrasting scenarios with even minor obstacles received passing scores. We observed that MT had the greatest chance of success when the task was simple and the language complexity needs were low.
READ LESS

Summary

This paper describes a new method for task-based speech-to-speech machine translation evaluation, in which tasks are defined and assessed according to independent published standards, both for the military tasks performed and for the foreign language skill levels used. We analyze task success rates and automatic MT evaluation scores (BLEU and...

READ MORE
Standardized ILR-based and task-based speech-to-speech MT evaluation

A language-independent approach to automatic text difficulty assessment for second-language learners

August 4, 2013
  |
Conference Paper
Author:
Wade Shen
…
Published in:
Proc. 2nd Workshop on Predicting and Improving Text Readability for Target Reader Populations, 4-9 August 2013.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words for Arabic, Dari, English, and Pashto. We compare Support Vector Machines and the Margin-Infused Relaxed Algorithm measured by mean squared error. We provide an analysis of which features are most predictive of a given level.
READ LESS

Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words...

READ MORE
A language-independent approach to automatic text difficulty assessment for second-language learners
1-5 of 5