From clark.3664 at buckeyemail.osu.edu  Wed Feb  1 21:38:47 2023
From: clark.3664 at buckeyemail.osu.edu (Clark, Christian)
Date: Thu, 2 Feb 2023 02:38:47 +0000
Subject: [CaCL]  babyLM shared task
Message-ID: <BL0PR01MB4611FE27FD17EE3F0C25E1ADD6D69@BL0PR01MB4611.prod.exchangelabs.com>

The new BabyLM shared task might of interest to people on this list. Call for papers: https://arxiv.org/pdf/2301.11796.pdf

Official site: https://babylm.github.io/

Description: We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome). We will release a shared evaluation pipeline which scores models on a variety of benchmarks and tasks, including targeted syntactic evaluations and natural language understanding.

----
Christian Clark
Ph.D. Student
Department of Linguistics
The Ohio State University
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From schuler.77 at osu.edu  Tue Feb  7 19:17:40 2023
From: schuler.77 at osu.edu (Schuler, William)
Date: Wed, 8 Feb 2023 00:17:40 +0000
Subject: [CaCL] reading for cacl
Message-ID: <SN6PR0102MB353312EF0118D5358437212D91D89@SN6PR0102MB3533.prod.exchangelabs.com>

Hi all,

Let's read this for thursday -- an introduction to CCG:

   https://homepages.inf.ed.ac.uk/steedman/papers/ccg/moravcsik2.pdf

wm
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From schuler.77 at osu.edu  Wed Feb  8 20:00:12 2023
From: schuler.77 at osu.edu (Schuler, William)
Date: Thu, 9 Feb 2023 01:00:12 +0000
Subject: [CaCL] Fwd: [Ling-Faculty] FW: [Folli] lecturer in Computational
 Linguistics in	UCL
In-Reply-To: <1E8CA3AD-A3BD-4457-AA7B-F2566FC44BEA@osu.edu>
References: <1E8CA3AD-A3BD-4457-AA7B-F2566FC44BEA@osu.edu>
Message-ID: <SN6PR0102MB3533635AB85D70D62169C07091D99@SN6PR0102MB3533.prod.exchangelabs.com>

may be of interest

________________________________
From: Ling-Faculty <ling-faculty-bounces+schuler=ling.ohio-state.edu at lists.osu.edu> on behalf of Levine, Robert via Ling-Faculty <ling-faculty at lists.osu.edu>
Sent: Thursday, January 26, 2023 7:52 AM
To: _ASC LING lingac <ASC-LING-lingac at osu.edu>
Cc: Levine, Robert <levine.1 at osu.edu>
Subject: [Ling-Faculty] FW: [Folli] lecturer in Computational Linguistics in UCL

FYI.

--- Bob
Dear colleagues, The Linguistics department of University College London (UCL) is recruiting a lecturer (permanent academic post) in Computational Linguistics. For more details of the position, see here: https:?//www.?ucl.?ac.?uk/work-at-ucl/search-ucl-jobs/details?jobId=3786&jobTitle=Lecturer%20in%20Linguistics%20(Computational%20Linguistics)
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Dear colleagues,



The Linguistics department of University College London (UCL) is recruiting a lecturer (permanent academic post) in Computational Linguistics.



For more details of the position, see here: https://www.ucl.ac.uk/work-at-ucl/search-ucl-jobs/details?jobId=3786&jobTitle=Lecturer%20in%20Linguistics%20(Computational%20Linguistics)<https://urldefense.com/v3/__https:/www.ucl.ac.uk/work-at-ucl/search-ucl-jobs/details?jobId=3786&jobTitle=Lecturer*20in*20Linguistics*20(Computational*20Linguistics)__;JSUlJQ!!KGKeukY!xAA4A_6H-q65SrEpis9KK9cYL9aRVj__0-gReZyojUv_DZ4IJ7BvFMZM7fhl1zFUZC8Vy7HC8MCyhC-AOJ5GXm-KS1Pbt6w$>




Best,
Mehrnoosh






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From schuler.77 at osu.edu  Mon Feb 13 20:54:37 2023
From: schuler.77 at osu.edu (Schuler, William)
Date: Tue, 14 Feb 2023 01:54:37 +0000
Subject: [CaCL] paper for thursday on TAG
Message-ID: <SN6PR0102MB3533B6185200325E5B77D20391A29@SN6PR0102MB3533.prod.exchangelabs.com>

Hi all,

Let's read this for thursday: https://repository.upenn.edu/cgi/viewcontent.cgi?article=1463&context=cis_reports

wm
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From oh.531 at buckeyemail.osu.edu  Fri Feb 17 10:32:29 2023
From: oh.531 at buckeyemail.osu.edu (Oh, Byung-Doh)
Date: Fri, 17 Feb 2023 15:32:29 +0000
Subject: [CaCL] CaCL 2/23: Intro to Combinatory Categorial Grammar part 2
Message-ID: <SJ2PR01MB8005322374D1CDEA3AEE003B96A19@SJ2PR01MB8005.prod.exchangelabs.com>

Hi everyone,

Next week, we will continue our discussion of CCG based on Steedman (2022): https://homepages.inf.ed.ac.uk/steedman/papers/ccg/moravcsik2.pdf.

Best,
Byung-Doh

=================
Byung-Doh Oh (he/him/his)
Ph.D. Student
Department of Linguistics
The Ohio State University

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From clark.3664 at buckeyemail.osu.edu  Thu Feb 23 14:17:56 2023
From: clark.3664 at buckeyemail.osu.edu (Clark, Christian)
Date: Thu, 23 Feb 2023 19:17:56 +0000
Subject: [CaCL]  Reading for 3/2
Message-ID: <BL0PR01MB46113ECCA76B478D2A5A89ACD6AB9@BL0PR01MB4611.prod.exchangelabs.com>

Hi everyone,

In next week's CaCL meeting we will discuss Meister and Cotterell 2021.

Title: Language Model Evaluation Beyond Perplexity

Link: https://aclanthology.org/2021.acl-long.414.pdf

Abstract:
We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework?paired with significance tests?for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type?token relationship of natural language than text produced using standard ancestral sampling; text from LSTMs reflects the natural language distributions over length, stopwords, and symbols surprisingly well.

----
Christian Clark
Ph.D. Student
Department of Linguistics
The Ohio State University
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