The Effectiveness of Structured Input and Structured Output on the Acquisition of Japanese Comparative Sentences

This research paper presents a quasi-experimental empirical study investigating the effects of structured input and structured output tasks on the acquisition of English causative forms. This research is framed on VanPatten’s (1996) input processing theory. The grammatical form chosen for this investigation is affected by a processing strategy called the First Noun Principle. There are three variables included that make this study significant. These variables are having participants that are young learners who speak Arabic as an L1 and using discourse-level instrumentation. These variables make this study significant because the studies that investigated the effectiveness of structured input practice with these variables are in the minority. The study’s main questions are: (i) What are the short-term effects of structured input and structured output on the acquisition of English causative forms as measured with discourse-level interpretation tasks? (ii) What are the short-term effects of structured input and structured output on the learners’ ability to acquire the English causative forms as measured with discourse-level production tasks? Participants were school-age learners (aged 12-13) from an Arabic background with Arabic as an L1 who studied English as a second language in Kuwait. A pre and post-test procedure was adopted in this study. Two instructional groups were created, which are: (i) structured input; (ii) structured output. Discourse-level tasks were used in the study to assess the effectiveness of the two instructional treatments. Results were analyzed using descriptive statistics and ANOVA. The main findings support the view that discourse-level structured input tasks are a useful pedagogical intervention in helping young L2 learners from an Arabic background with Arabic as an L1 to process, interpret and produce accurate English causative forms. The main findings have theoretical and pedagogical implications for language learning and teaching.

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The relative effects of isolated and combined structured input and structured output on the acquisition of the English causative forms

IRAL - International Review of Applied Linguistics in Language Teaching ◽

10.1515/iral-2016-0038 ◽

2019 ◽

Vol 57 (3) ◽

pp. 265-287 ◽

Cited By ~ 5

Author(s):

Alessandro Benati ◽

Maria Batziou

Keyword(s):

Control Group ◽

Production Task ◽

Processing Instruction ◽

Structured Input ◽

Sentence Level ◽

Post Test ◽

Structured Output ◽

Causative Structure ◽

Based Instruction ◽

Output Only

AbstractThe present study explores the effects of structured input and structured output when delivered in isolation or in combination on the acquisition of the English causative. Research investigating the effects of processing instruction and meaning output-based instruction has provided some interesting and sometimes conflicting results. Additionally, there are a number of issues (e. g., measuring a combination of structured input and structured output, measuring discourse-level effects) that have not been fully and clearly addressed. To provide answers to the questions formulated in this study, two classroom experiments were carried out. In the first study, fifty-four Chinese university students (age 18–20) participated. The participants were randomly assigned to four groups: structured input only group (n=13); structured output only group (n=15); combined structured input and structured output group (n=16); control group (n=10). In the second study, thirty school-age Greek learners (age 10–12) participated. The participants were randomly assigned to three groups: structured input only group (n=10); structured output only group (n=10); combined structured input and structured output group (n=10).Only subjects who participated in all phases of each experiment and scored lower than 60 % in the pre-tests were included in the final data collection. Instruction lasted for three hours. The control group received no instruction on the causative structure. Interpretation and production tasks were used in a pre-test and post-test design. The design included a delayed post-test battery (3 weeks after instruction) for both experiments. In the first study, the assessment tasks included an interpretation and production task at sentence-level, and an interpretation task at discourse-level. In the second study, an additional discourse-level production task was adopted along with the interpretation discourse-level task. The results indicated that learners who received structured input both in isolation and in combination benefitted more than learners receiving structured output only. These two groups were able to retain instructional gains three weeks later in all assessment measures.

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Discourse and long-term effects of isolated and combined structured input and structured output on the acquisition of the English causative form

Language Awareness ◽

10.1080/09658416.2019.1604721 ◽

2019 ◽

Vol 28 (2) ◽

pp. 77-96

Author(s):

Alessandro Benati ◽

Maria Batziou

Keyword(s):

Long Term Effects ◽

Structured Input ◽

Structured Output

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Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/713 ◽

2019 ◽

Author(s):

Chao Ma ◽

F A Rezaur Rahman Chowdhury ◽

Aryan Deshwal ◽

Md Rakibul Islam ◽

Janardhan Rao Doppa ◽

...

Keyword(s):

Structured Prediction ◽

Greedy Search ◽

Computationally Efficient ◽

Structured Input ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Training Approach ◽

Complex Models ◽

Simple Class ◽

Structured Output

In a structured prediction problem, we need to learn a predictor that can produce a structured output given a structured input (e.g., part-of-speech tagging). The key learning and inference challenge is due to the exponential size of the structured output space. This paper makes four contributions towards the goal of a computationally-efficient inference and training approach for structured prediction that allows to employ complex models and to optimize for non-decomposable loss functions. First, we define a simple class of randomized greedy search (RGS) based inference procedures that leverage classification algorithms for simple outputs. Second, we develop a RGS specific learning approach for amortized inference that can quickly produce high-quality outputs for a given set of structured inputs. Third, we plug our amortized RGS inference solver inside the inner loop of parameter-learning algorithms (e.g., structured SVM) to improve the speed of training. Fourth, we perform extensive experiments on diverse structured prediction tasks. Results show that our proposed approach is competitive or better than many state-of-the-art approaches in spite of its simplicity.

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Special issue on statistical learning of natural language structured input and output

Natural Language Engineering ◽

10.1017/s135132491200006x ◽

2012 ◽

Vol 18 (2) ◽

pp. 147-153

Author(s):

LLUÍS MÀRQUEZ ◽

ALESSANDRO MOSCHITTI

Keyword(s):

Machine Learning ◽

Natural Language ◽

Statistical Learning ◽

Computational Linguistics ◽

Original Form ◽

Complex Data ◽

Input Output ◽

Special Issue ◽

Structured Input ◽

Structured Output

AbstractDuring last decade, machine learning and, in particular, statistical approaches have become more and more important for research in Natural Language Processing (NLP) and Computational Linguistics. Nowadays, most stakeholders of the field use machine learning, as it can significantly enhance both system design and performance. However, machine learning requires careful parameter tuning and feature engineering for representing language phenomena. The latter becomes more complex when the system input/output data is structured, since the designer has both to (i) engineer features for representing structure and model interdependent layers of information, which is usually a non-trivial task; and (ii) generate a structured output using classifiers, which, in their original form, were developed only for classification or regression. Research in empirical NLP has been tackling this problem by constructing output structures as a combination of the predictions of independent local classifiers, eventually applying post-processing heuristics to correct incompatible outputs by enforcing global properties. More recently, some advances of the statistical learning theory, namely structured output spaces and kernel methods, have brought techniques for directly encoding dependencies between data items in a learning algorithm that performs global optimization. Within this framework, this special issue aims at studying, comparing, and reconciling the typical domain/task-specific NLP approaches to structured data with the most advanced machine learning methods. In particular, the selected papers analyze the use of diverse structured input/output approaches, ranging from re-ranking to joint constraint-based global models, for diverse natural language tasks, i.e., document ranking, syntactic parsing, sequence supertagging, and relation extraction between terms and entities. Overall, the experience with this special issue shows that, although a definitive unifying theory for encoding and generating structured information in NLP applications is still far from being shaped, some interesting and effective best practice can be defined to guide practitioners in modeling their own natural language application on complex data.

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Structured Input vs. Structured Output Task’s Effects on the Acquisition of the English Causative Forms: Discourse-Level. Structured Input vs. Structured Output Task’s Effects on the Acquisition of the English Causative Forms: Discourse-Level.

Arab World English Journal ◽

10.24093/awej/vol12no1.19 ◽

2021 ◽

Vol 12 (1) ◽

pp. 270-292

Author(s):

Najat Alabdullah

Keyword(s):

Language Learning ◽

Test Procedure ◽

Short Term ◽

Learning And Teaching ◽

Structured Input ◽

Processing Theory ◽

Post Test ◽

Quasi Experimental ◽

Structured Output ◽

Short Term Effects

This research paper presents a quasi-experimental empirical study investigating the effects of structured input and structured output tasks on the acquisition of English causative forms. This research is framed on VanPatten’s (1996) input processing theory. The grammatical form chosen for this investigation is affected by a processing strategy called the First Noun Principle. There are three variables included that make this study significant. These variables are having participants that are young learners who speak Arabic as an L1 and using discourse-level instrumentation. These variables make this study significant because the studies that investigated the effectiveness of structured input practice with these variables are in the minority. The study’s main questions are: (i) What are the short-term effects of structured input and structured output on the acquisition of English causative forms as measured with discourse-level interpretation tasks? (ii) What are the short-term effects of structured input and structured output on the learners’ ability to acquire the English causative forms as measured with discourse-level production tasks? Participants were school-age learners (aged 12-13) from an Arabic background with Arabic as an L1 who studied English as a second language in Kuwait. A pre and post-test procedure was adopted in this study. Two instructional groups were created, which are: (i) structured input; (ii) structured output. Discourse-level tasks were used in the study to assess the effectiveness of the two instructional treatments. Results were analyzed using descriptive statistics and ANOVA. The main findings support the view that discourse-level structured input tasks are a useful pedagogical intervention in helping young L2 learners from an Arabic background with Arabic as an L1 to process, interpret and produce accurate English causative forms. The main findings have theoretical and pedagogical implications for language learning and teaching.

Download Full-text