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서울대학교 인지과학연구소> Journal of Cognitive Science> Assisting Authors to Convert Raw Products into Polished Prose

KCI후보SCOUPUS

Assisting Authors to Convert Raw Products into Polished Prose

Takumi Ito , Tatsuki Kuribayashi , Hayato Kobayashi , Ana Brassard , Masato Hagiwara , Jun Suzuki , Kentaro Inui
  • : 서울대학교 인지과학연구소
  • : Journal of Cognitive Science 21권1호
  • : 연속간행물
  • : 2020년 03월
  • : 101-137(37pages)

DOI


목차

1. Introduction
2. The Sentence-level Revision task
3. The SMITH dataset
4. Analysis of the SMITH dataset
5. Experiments
6. Results
7. Related work
8. Conclusion and future work
Acknowledgements
References

키워드 보기


초록 보기

Being a notoriously complex problem, writing is generally decomposed into a series of subtasks: idea generation, expression, revision, etc. Given some goal, the author generates a set of ideas (brainstorming), which he integrates into some skeleton (outline, text plan, outline). This leads to a first draft which is submitted then for revision possibly yielding changes at various levels (content, structure, form). Having made a draft, authors usually revise, edit, and proofread their documents.
We confine ourselves here only to academic writing, focusing on sentence production. While there has been quite some work on this topic, most writing assistance has mainly dealt with grammatical errors, editing and proofreading, the goal being the correction of surface-level problems such as typography, spelling, or grammatical errors.
We broaden the scope by also including cases where the entire sentence needs to be rewritten in order to express properly all of the information planned. Hence, Sentence-level Revision (SentRev) becomes part of our writing assistance task.
Obviously, systems performing well in this task can be of considerable help for inexperienced authors by producing fluent, well-formed sentences based on the user’s drafts.
In order to evaluate our SentRev model, we have built a new, freely available crowdsourced evaluation dataset which consists of a set of incomplete sentences produced by nonnative writers paired with final version sentences extracted from published academic papers. We also used this dataset to establish baseline performance on SentRev.

UCI(KEPA)

간행물정보

  • : 사회과학분야  > 심리학
  • : KCI후보
  • : SCOPUS
  • : 계간
  • : 1598-2327
  • :
  • : 학술지
  • : 연속간행물
  • : 2000-2020
  • : 290


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발행기관 최신논문
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1Introduction to the Special Issue: AI at the Crossroads of NLP and Neurosciences

저자 : Michael Zock

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 1-13 (13 pages)

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(기관인증 필요)

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초록보기

2Data-driven models and computational tools for neurolinguistics: a language technology perspective

저자 : Moscow Russia

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 15-52 (38 pages)

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In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as word embeddings and pre-trained language models. Mutual enrichment of neurolinguistics and language technologies leads to development of brain-aware natural language representations. The importance of this research area is emphasized by medical applications.

3A Unified Hierarchy for AI and Natural Intelligence through Auto-Programming for General Purposes

저자 : Juyang Weng

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 53-99 (47 pages)

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Despite the current great public interest in neural network based artificial intelligence, there exists a huge gap between artificial intelligence (AI) and natural intelligence (NI). Furthermore, there is such a lack of united hierarchy of intelligence that intelligence is widely regarded piecemeal. This status quo results in highly brittle AI systems. This paper proposes how an autonomous agent, natural or artificial, develops a united intelligence hierarchy in the brain. The term "united" means not only for AI and NI both, but also for all practical sensory modalities and motor modalities, including perception, representation/reasoning, learning, societal activities and politics. This line of work has been supported by rigorous mathematical proofs and initial experimental verications.
However, this paper minimizes mathematical material so that the new in- formation here can reach a wide audience. We should ask a new and general question: "How can a machine, natural or artificial, Autonomously Program For General Purposes (APFGP) from the real physical world?"
We have given this question a solution, theoretically, experimentally, and mathematically. A clear but powerful learning engine|Developmental Network (DN) enables APFGP. Hopefully, understanding APFGP for both AI and NI not only fully automate constructions of AI systems but also improve human development, individually and societally.

4Assisting Authors to Convert Raw Products into Polished Prose

저자 : Takumi Ito , Tatsuki Kuribayashi , Hayato Kobayashi , Ana Brassard , Masato Hagiwara , Jun Suzuki , Kentaro Inui

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 101-137 (37 pages)

다운로드

(기관인증 필요)

초록보기

Being a notoriously complex problem, writing is generally decomposed into a series of subtasks: idea generation, expression, revision, etc. Given some goal, the author generates a set of ideas (brainstorming), which he integrates into some skeleton (outline, text plan, outline). This leads to a first draft which is submitted then for revision possibly yielding changes at various levels (content, structure, form). Having made a draft, authors usually revise, edit, and proofread their documents.
We confine ourselves here only to academic writing, focusing on sentence production. While there has been quite some work on this topic, most writing assistance has mainly dealt with grammatical errors, editing and proofreading, the goal being the correction of surface-level problems such as typography, spelling, or grammatical errors.
We broaden the scope by also including cases where the entire sentence needs to be rewritten in order to express properly all of the information planned. Hence, Sentence-level Revision (SentRev) becomes part of our writing assistance task.
Obviously, systems performing well in this task can be of considerable help for inexperienced authors by producing fluent, well-formed sentences based on the user's drafts.
In order to evaluate our SentRev model, we have built a new, freely available crowdsourced evaluation dataset which consists of a set of incomplete sentences produced by nonnative writers paired with final version sentences extracted from published academic papers. We also used this dataset to establish baseline performance on SentRev.

5Computational Representation of Chinese Characters: Comparison Between Singular Value Decomposition and Variational Autoencoder

저자 : Yu-hsiang Tseng , Shu-kai Hsieh

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 139-158 (20 pages)

다운로드

(기관인증 필요)

초록보기

Being a notoriously complex problem, writing is generally decomposed into a series of subtasks: idea generation, expression, revision, etc. Given some goal, the author generates a set of ideas (brainstorming), which he integrates into some skeleton (outline, text plan, outline). This leads to a first draft which is submitted then for revision possibly yielding changes at various levels (content, structure, form). Having made a draft, authors usually revise, edit, and proofread their documents.
We confine ourselves here only to academic writing, focusing on sentence production. While there has been quite some work on this topic, most writing assistance has mainly dealt with grammatical errors, editing and proofreading, the goal being the correction of surface-level problems such as typography, spelling, or grammatical errors.
We broaden the scope by also including cases where the entire sentence needs to be rewritten in order to express properly all of the information planned. Hence, Sentence-level Revision (SentRev) becomes part of our writing assistance task. Obviously, systems performing well in this task can be of considerable help for inexperienced authors by producing fluent, well-formed sentences based on the user's drafts.
In order to evaluate our SentRev model, we have built a new, freely available crowdsourced evaluation dataset which consists of a set of incomplete sentences produced by nonnative writers paired with final version sentences extracted from published academic papers. We also used this dataset to establish baseline performance on SentRev.

6NLP's Clever Hans Moment has Arrived

저자 : Benjamin Heinzerling

발행기관 : 서울대학교 인지과학연구소 간행물 : Journal of Cognitive Science 21권 1호 발행 연도 : 2020 페이지 : pp. 159-167 (9 pages)

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(기관인증 필요)

초록보기

Large, pretrained language models have led to a flurry of new state-of-the-art results being reported in many areas of natural language processing. However, recent work has also shown that such models tend to solve language tasks by relying on superficial cues found in benchmark datasets, instead of acquiring the capabilities envisioned by the task designers. In this short opinion piece, I review a report by Niven & Kao (2019) of this so-called Clever Hans effect on an argument reasoning task and discuss possible solutions for its prevention.

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