scholarly journals An Introduction to Grammatical Inference for Linguists

Triangle ◽  
2018 ◽  
pp. 1
Author(s):  
Leonor Becerra-Bonache
Keyword(s):  
Machine Learning ◽  
Language Acquisition ◽  
Natural Language ◽  
Language Learning ◽  
Computer Science ◽  
Special Interest ◽  
Formal Languages ◽  
Grammatical Inference ◽  
Basic Concepts

This paper is meant to be an introductory guide to Grammatical Inference (GI), i.e., the study of machine learning of formal languages. It is designed for non-specialists in Computer Science, but with a special interest in language learning. It covers basic concepts and models developed in the framework of GI, and tries to point out the relevance of these studies for natural language acquisition.

scholarly journals Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning

2013 ◽  
Vol 1 ◽  
pp. 315-326 ◽  
Author(s):  
Minh-Thang Luong ◽  
Michael C. Frank ◽  
Mark Johnson
Keyword(s):  
Language Acquisition ◽  
Language Learning ◽  
Long Strings ◽  
Grammatical Inference ◽  
Independence Assumption ◽  
Structure Information ◽  
Acquisition Task ◽  
Parse Trees ◽  
Input Strings ◽  
Inference Task

Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context of language acquisition, this independence assumption discards cues that are important to the learner, e.g., the fact that consecutive utterances are likely to share the same referent (Frank et al., 2013). The current paper describes an approach to the problem of simultaneously modeling grounded language at the sentence and discourse levels. We combine ideas from parsing and grammar induction to produce a parser that can handle long input strings with thousands of tokens, creating parse trees that represent full discourses. By casting grounded language learning as a grammatical inference task, we use our parser to extend the work of Johnson et al. (2012), investigating the importance of discourse continuity in children’s language acquisition and its interaction with social cues. Our model boosts performance in a language acquisition task and yields good discourse segmentations compared with human annotators.

IDAZKIDE: an intelligent CALL environment for second language acquisition

ReCALL ◽  
1999 ◽  
Vol 11 (S1) ◽  
pp. 12-19
Author(s):  
Arantza Díaz de llarraza ◽  
Aitor Maritxalar ◽  
Montse Maritxalar ◽  
Maite Oronoz
Keyword(s):  
Second Language ◽  
Natural Language Processing ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Learning Environment ◽  
Natural Language ◽  
Language Learning ◽  
Language Processing ◽  
Student Model ◽  
Advice Taking

This paper presents IDAZKIDE, a prototype of an intelligent language learning environment (ICALL) for learners of Basque. The philosophy of the system is to make different Natural Language Processing tools simultaneously accessible to students to help them (mainly at the morphological level) to write in Basque, as well as to give advice, taking into account some characteristics of the student gathered in a student model.

scholarly journals Developmental Language Learning from Human/Humanoid Robot Social Interactions

Robotics ◽  
2013 ◽  
pp. 1328-1353 ◽  
Author(s):  
Artur M. Arsénio
Keyword(s):  
Machine Learning ◽  
Social Context ◽  
Language Acquisition ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Computational Models ◽  
Robot Learning ◽  
Psychological Models

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.

scholarly journals Grounded Language Learning: Where Robotics and NLP Meet

2018 ◽  
Author(s):  
Cynthia Matuszek
Keyword(s):  
Language Acquisition ◽  
Natural Language ◽  
Language Learning ◽  
Physical World ◽  
Research Area ◽  
Embodied Agents ◽  
Future Directions ◽  
Recent Advances ◽  
The World ◽  
And Control

Grounded language acquisition is concerned with learning the meaning of language as it applies to the physical world. As robots become more capable and ubiquitous, there is an increasing need for non-specialists to interact with and control them, and natural language is an intuitive, flexible, and customizable mechanism for such communication. At the same time, physically embodied agents offer a way to learn to understand natural language in the context of the world to which it refers. This paper gives an overview of the research area, selected recent advances, and some future directions and challenges that remain.

scholarly journals Do children privilege phonological cues in noun class learning?

2018 ◽  
Author(s):  
Jennifer Culbertson ◽  
Kenny Smith ◽  
Hanna Jarvinen ◽  
Frances Haggarty
Keyword(s):  
Language Acquisition ◽  
Natural Language ◽  
Language Learning ◽  
Grammatical Gender ◽  
Developmental Changes ◽  
Artificial Language ◽  
Semantic Cues ◽  
Artificial Language Learning ◽  
Noun Class

Previous research on acquisition of noun class systems, such as grammatical gender, has shown that child learners rely disproportionately on phonological cues to class, even when competing semantic cues are more reliable. Culbertson, Gagliardi, and Smith (2017) use artificial language learning experiments with adults to argue that over-reliance on phonology may be due to the fact that phonological cues are available first; learners base early representations on surface phonological dependencies, only later integrating semantic cues from noun meanings. Here, we show that child learners (6-7 year-olds) show this same sensitivity to early availability. However, we also find intriguing evidence of developmental changes in sensitivity to semantics; when both cues are simultaneously available children are more likely to rely on a phonology cue than adults. Our results suggest that early availability and a bias in favor of phonological cues may both contribute to children’s over- reliance on phonology in natural language acquisition.

Developmental Language Learning from Human/Humanoid Robot Social Interactions

2013 ◽  
pp. 197-223
Author(s):  
Artur M. Arsénio
Keyword(s):  
Machine Learning ◽  
Social Context ◽  
Language Acquisition ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Computational Models ◽  
Robot Learning ◽  
Psychological Models

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.

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