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Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Tal Linzen, Emmanuel Dupoux, Yoav Goldberg
<span title="">2016</span> <i title="MIT Press - Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jtrizn7izbhdlfmye6eriw6ub4" style="color: black;">Transactions of the Association for Computational Linguistics</a> </i> &nbsp;
The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities.  ...  We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models.  ...  This indicates that the network generalized the dependency from the common distances of 0 and 1 to rare distances of 10 and more.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/tacl_a_00115">doi:10.1162/tacl_a_00115</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5aar4mgtnncu3nj75hjzmukzhq">fatcat:5aar4mgtnncu3nj75hjzmukzhq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180422131215/https://www.transacl.org/ojs/index.php/tacl/article/viewFile/972/215" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/a5/3aa52436575cf6768a0a1a476601825f6a62e58f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1162/tacl_a_00115"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mitpressjournals.org </button> </a>

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies [article]

Tal Linzen, Emmanuel Dupoux, Yoav Goldberg
<span title="2016-11-04">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities.  ...  We probe the architecture's grammatical competence both using training objectives with an explicit grammatical target (number prediction, grammaticality judgments) and using language models.  ...  This indicates that the network generalized the dependency from the common distances of 0 and 1 to rare distances of 10 and more.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.01368v1">arXiv:1611.01368v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5jyhlvdyzbh5llcda7hqz427qa">fatcat:5jyhlvdyzbh5llcda7hqz427qa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200914014459/https://arxiv.org/pdf/1611.01368v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/37/70/3770300cd0492e7438440225720b4a0f087224b4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.01368v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Colorless green ideas do sleep furiously: gradient acceptability and the nature of the grammar

Jon Sprouse, Beracah Yankama, Sagar Indurkhya, Sandiway Fong, Robert C. Berwick
<span title="2018-09-25">2018</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/czmelxdnhfaz7nzrlyhy5djfva" style="color: black;">The Linguistic Review</a> </i> &nbsp;
To make their case, they present the results of correlating the output of several probabilistic models trained solely on naturally occurring sentences with the gradient acceptability judgments that humans  ...  Grammaticality, acceptability, and probability: A prob- abilistic view of linguistic knowledge. Cognitive Science 41(5):1201–1241).  ...  Acknowledgements: We would like to thank two anonymous reviewers for immensely helpful for comments on an earlier draft.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1515/tlr-2018-0005">doi:10.1515/tlr-2018-0005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g67xihanebhl3c6656wvjblcte">fatcat:g67xihanebhl3c6656wvjblcte</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201103235313/https://dspace.mit.edu/bitstream/handle/1721.1/125923/%5BThe%20Linguistic%20Review%5D%20Colorless%20green%20ideas%20do%20sleep%20furiously%20gradient%20acceptability%20and%20the%20nature%20of%20the%20grammar.pdf;jsessionid=C8682D41F90BF60B900BE51083B58DCB?sequence=2" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ff/d5/ffd57be37ba03b0fc781b00cbf673cc19355b7be.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1515/tlr-2018-0005"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> degruyter.com </button> </a>

Can Recurrent Neural Networks Validate Usage-Based Theories of Grammar Acquisition?

Ludovica Pannitto, Aurelie Herbelot
<span title="2022-03-23">2022</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5r5ojcju2repjbmmjeu5oyawti" style="color: black;">Frontiers in Psychology</a> </i> &nbsp;
This mini-review gives an overview of the state of the field, focusing on the influence of the theoretical framework in the interpretation of results.  ...  It has been shown that Recurrent Artificial Neural Networks automatically acquire some grammatical knowledge in the course of performing linguistic prediction tasks.  ...  for the network to predict long-distance number agreement.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fpsyg.2022.741321">doi:10.3389/fpsyg.2022.741321</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35401316">pmid:35401316</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8984258/">pmcid:PMC8984258</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7gpnm4aolbbmrkf3w7d3mzlifu">fatcat:7gpnm4aolbbmrkf3w7d3mzlifu</a> </span>
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Exploiting Unlabeled Data for Neural Grammatical Error Detection [article]

Zhuoran Liu, Yang Liu
<span title="2016-11-29">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected.  ...  Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because  ...  The first problem with this method is that it is incapable of capturing long-distance dependency.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.08987v2">arXiv:1611.08987v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x5aoznel3vcxtjddecjbclotae">fatcat:x5aoznel3vcxtjddecjbclotae</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907041311/https://arxiv.org/pdf/1611.08987v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3e/08/3e089c6c3f9e8f4a243f0106f23d31dfcaa34a88.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1611.08987v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

BLiMP: The Benchmark of Linguistic Minimal Pairs for English [article]

Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
<span title="2020-09-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We find that state-of-the-art models identify morphological contrasts reliably, but they struggle with semantic restrictions on the distribution of quantifiers and negative polarity items and subtle syntactic  ...  We introduce The Benchmark of Linguistic Minimal Pairs (shortened to BLiMP), a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.00582v3">arXiv:1912.00582v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hdm2cbnshjg6rjoxuzf4pg7q4q">fatcat:hdm2cbnshjg6rjoxuzf4pg7q4q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200926001053/https://arxiv.org/pdf/1912.00582v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.00582v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Sentence-Level Fluency Evaluation: References Help, But Can Be Spared! [article]

Katharina Kann, Sascha Rothe, Katja Filippova
<span title="2018-09-24">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency  ...  We show that ROUGE-LM yields a significantly higher correlation with human judgments than all baseline metrics, including WPSLOR on its own.  ...  We calculate the probability of a sentence with a long-short term memory (LSTM, Hochreiter and Schmidhuber (1997) ) LM, i.e., a special type of RNN LM, which has been trained on a large corpus.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.08731v1">arXiv:1809.08731v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fjwkjy4tnzfdpcvc5wlyxyzwn4">fatcat:fjwkjy4tnzfdpcvc5wlyxyzwn4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200828015211/https://arxiv.org/pdf/1809.08731v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6f/49/6f494f178d5d514609fe55bd823904a1b9f74e50.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1809.08731v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!

Katharina Kann, Sascha Rothe, Katja Filippova
<span title="">2018</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ot5sbt27tzdyrhcooo2wxlw7ki" style="color: black;">Proceedings of the 22nd Conference on Computational Natural Language Learning</a> </i> &nbsp;
Motivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency  ...  We show that ROUGE-LM yields a significantly higher correlation with human judgments than all baseline metrics, including WPSLOR on its own.  ...  We calculate the probability of a sentence with a long-short term memory (LSTM, Hochreiter and Schmidhuber (1997) ) LM, i.e., a special type of RNN LM, which has been trained on a large corpus.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/k18-1031">doi:10.18653/v1/k18-1031</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/conll/KannRF18.html">dblp:conf/conll/KannRF18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vp2fxiceqbghjnl6fx4jdlet54">fatcat:vp2fxiceqbghjnl6fx4jdlet54</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200505231956/https://www.aclweb.org/anthology/K18-1031.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/09/0a09c2df32cea2d7e5e1a202b1fada5f9aa26a00.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/k18-1031"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Multi-turn Dialogue Model Based on the Improved Hierarchical Recurrent Attention Network

<span title="2021-07-22">2021</span> <i title="Faculty of Civil Engineering, Architecture and Geodesy, University of Split"> International Journal for Engineering Modelling </i> &nbsp;
At present, HRAN, one of the most advanced models for multi-turn dialogue problems, uses a hierarchical recurrent encoder-decoder combined with a hierarchical attention mechanism.  ...  To solve this problem, we proposed an improved hierarchical recurrent attention network, a self-attention network (HSAN), instead of RNN, to learn word representations and utterances representations.  ...  Experiments show that the model can reduce computation and improve parallel efficiency, and self-attention is superior in capturing long-distance dependence.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31534/engmod.2021.2.ri.02d">doi:10.31534/engmod.2021.2.ri.02d</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mrbjmnrqhfb4fo2ohmtvyneehy">fatcat:mrbjmnrqhfb4fo2ohmtvyneehy</a> </span>
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Cross-Linguistic Syntactic Evaluation of Word Prediction Models [article]

Aaron Mueller, Garrett Nicolai, Panayiota Petrou-Zeniou, Natalia Talmina, Tal Linzen
<span title="2020-05-21">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy.  ...  Across languages, monolingual LSTMs achieved high accuracy on dependencies without attractors, and generally poor accuracy on agreement across object relative clauses.  ...  Acknowledgments This material is based on work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 1746891.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.00187v2">arXiv:2005.00187v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zhlwi3wcwnb2lg5n3xcnmwb5de">fatcat:zhlwi3wcwnb2lg5n3xcnmwb5de</a> </span>
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Image Captioning Based on Deep Neural Networks

Shuang Liu, Liang Bai, Yanli Hu, Haoran Wang, Yansong Wang
<span title="">2018</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4vlgvitw6fcmbay5hkyo2s2ime" style="color: black;">MATEC Web of Conferences</a> </i> &nbsp;
Image captioning is a representative of this filed, which makes the computer learn to use one or more sentences to understand the visual content of an image.  ...  In this paper, we mainly describe three image captioning methods using the deep neural networks: CNN-RNN based, CNN-CNN based and Reinforcement-based framework.  ...  In this principle, the long sequence dependency problem in the neural network can be solved. Vinyals et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/201823201052">doi:10.1051/matecconf/201823201052</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a2qt4hcqojahzdr3bopco3dd6y">fatcat:a2qt4hcqojahzdr3bopco3dd6y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430135231/https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_01052.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/bc/39bc39c1681f2558bf4990942b1222d1c5f108f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/201823201052"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Sentiment Classification Based on Part-of-Speech and Self-Attention Mechanism

Kefei Cheng, Yanan Yue, Zhiwen Song
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
In addition, our innovative introduction of the Focal Loss effectively alleviates the impact of sample imbalance on model performance.  ...  We conduct substantial experiments on various datasets, and the encouraging results indicate the efficacy of our proposed approach.  ...  The difference between RNNs and CNN based model is that RNNs are better at modeling long-distance semantics in text and capturing contextual information.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2967103">doi:10.1109/access.2020.2967103</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tvmf7ut7hvdbdotj3rclqfop7y">fatcat:tvmf7ut7hvdbdotj3rclqfop7y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107183232/https://ieeexplore.ieee.org/ielx7/6287639/8948470/08962060.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/da/54/da54211d24a2138ed7e94364ad3b413dfb23656c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2967103"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Adversarial Machine Learning in Text Processing: A Literature Survey

Izzat Alsmadi, Nura Aljaafari, Mahmoud Nazzal, Shadan Alhamed, Ahmad H. Sawalmeh, Conrado P. Vizcarra, Abdallah Khreishah, Muhammad Anan, Abdulelah Algosaibi, Mohammed Abdulaziz Al-Naeem, Adel Aldalbahi, Abdulaziz Al-Humam
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Our study showed that as applications of text generation will continue to grow in the near future, the type and nature of attacks on those applications and their machine learning algorithms will continue  ...  This usage will allow for a seamless lexical and grammatical transition between various writing styles.  ...  Still, a drawback of RNNs is their poor performance when the learning sequences have long-term temporal dependence [56] .  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220223094052/https://ieeexplore.ieee.org/ielx7/6287639/9668973/09693527.pdf?tp=&amp;arnumber=9693527&amp;isnumber=9668973&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a0/e4/a0e4eb43f01bc4a011dd0237f53a77a5c223410f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2022.3146405"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Linguistic generalization and compositionality in modern artificial neural networks [article]

Marco Baroni
<span title="2019-06-20">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
After reviewing the main innovations characterizing current deep language processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations  ...  I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it offers a new perspective on possible computational  ...  RNN simulations of gram- maticality judgments on long-distance dependencies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.00157v2">arXiv:1904.00157v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ja6wz474vfmlllijyz5haby6e">fatcat:7ja6wz474vfmlllijyz5haby6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200904144836/https://arxiv.org/pdf/1904.00157v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e4/e4/e4e488c5f3f09d6c1cb135d4df302e274d9bf23e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.00157v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Lossy‐Context Surprisal: An Information‐Theoretic Model of Memory Effects in Sentence Processing

Richard Futrell, Edward Gibson, Roger P. Levy
<span title="2020-02-26">2020</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sjomsvi4zngnnh4gx5bz2onwye" style="color: black;">Cognitive Science</a> </i> &nbsp;
A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context.  ...  We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language-dependent structural forgetting, where the effects of memory  ...  But if the representation of threw has been in working memory for a long time -corresponding to the long dependency-then this retrieval operation might be difficult or inaccurate, and moreover the difficulty  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/cogs.12814">doi:10.1111/cogs.12814</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32100918">pmid:32100918</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7065005/">pmcid:PMC7065005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oliinwtbojcizcrv7yb25whv7m">fatcat:oliinwtbojcizcrv7yb25whv7m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200511185241/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7065005&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1a/ce/1aceaf2d8467deb9808cab893e8b75c6be5f9e2b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/cogs.12814"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065005" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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