Analyzing Learner Understanding of Novel L2 Vocabulary

Knowles, Rebecca; Renduchintala, Adithya; Koehn, Philipp; Eisner, Jason

doi:10.18653/v1/k16-1013

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Labutov and Lipson (2014) carry out experiments to determine the guessability of a word in code switched text. A similar work by Knowles et al (2016) discuss the factors that can potentially affect the guessability of a German word with English context. We extend these works to model acquisition in multiple languages: English-Spanish, Spanish-English and French-English.…”

Section: Related Workmentioning

confidence: 99%

Context Based Approach for Second Language Acquisition

Nayak¹,

Rao²

2018

Proceedings of the Thirteenth Workshop on Innovative Use of NLP For Building Educational Applications

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SLAM 2018 focuses on predicting a student's mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks-English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features play a major role in language acquisition modeling. Our model beats Duolingo's baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.

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Section: Related Workmentioning

confidence: 99%

Context Based Approach for Second Language Acquisition

Nayak¹,

Rao²

2018

Proceedings of the Thirteenth Workshop on Innovative Use of NLP For Building Educational Applications

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“…into their model to rank search results by their reading level. Although not directly about ARA, Knowles et al (2016) explored the relationship between a word comprehension and a learner's native language. However, though ARA approaches are meant to be for real users in most of the cases, we don't see much work on modeling user features in relation to ARA.…”

Section: Readability Modelmentioning

confidence: 99%

Trends, Limitations and Open Challenges in Automatic Readability Assessment Research

Vajjala¹

2021

Preprint

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Readability assessment is the task of evaluating the reading difficulty of a given piece of text. Although research on computational approaches to readability assessment is now two decades old, there is not much work on synthesizing this research. This article is a brief survey of contemporary research on developing computational models for readability assessment. We identify the common approaches, discuss their shortcomings, and identify some challenges for the future.Where possible, we also connect computational research with insights from related work in other disciplines such as education and psychology.

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“…Decoding methods. While greedy decoding and beam search are popular strategies for sequenceto-sequence tasks, such as machine translation, Knowles et al (2016) and Stahlberg and Byrne (2019) showed that searching for the most probable sentence in a model trained with likelihood maximization has a bias for short sentences. In open-ended generation, Fan et al (2018) and Holtzman et al (2018Holtzman et al ( , 2019 have shown that these methods lead to repetitions and dull text.…”

Section: Related Workmentioning

confidence: 99%

Sparse Text Generation

Martins¹,

Marinho²,

Martins³

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Current state-of-the-art text generators build on powerful language models such as GPT-2, achieving impressive performance. However, to avoid degenerate text, they require sampling from a modified softmax, via temperature parameters or ad-hoc truncation techniques, as in top-k or nucleus sampling. This creates a mismatch between training and testing conditions. In this paper, we use the recently introduced entmax transformation to train and sample from a natively sparse language model, avoiding this mismatch. The result is a text generator with favorable performance in terms of fluency and consistency, fewer repetitions, and n-gram diversity closer to human text. In order to evaluate our model, we propose three new metrics for comparing sparse or truncated distributions: -perplexity, sparsemax score, and Jensen-Shannon divergence. Human-evaluated experiments in story completion and dialogue generation show that entmax sampling leads to more engaging and coherent stories and conversations. Entmax SamplingKey to our method is the recently proposed αentmax family of transformations 2 (Peters et al., 2019), parametrized by a scalar parameter α ≥ 1: α-entmax(z t ) := argmax p∈ d

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Analyzing Learner Understanding of Novel L2 Vocabulary

Cited by 6 publications

References 11 publications

Context Based Approach for Second Language Acquisition

Context Based Approach for Second Language Acquisition

Trends, Limitations and Open Challenges in Automatic Readability Assessment Research

Sparse Text Generation

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