(apologies for cross-posting) ---------------------------------------------------------------- *Workshop for NLP Open Source Software (NLP-OSS)* 11 or 12 Nov 2018, Co-located with EMNLP 2020https://nlposs.github.io/2020
Deadline for Long and Short Paper submission: 05 August 2020 (23:59, GMT-11) ---------------------------------------------------------------- You have used NLP open source tools and bore grievances but found the solution after hours of coffee and computer staring. Share that at NLP-OSS and suggest how open source could change for the better (e.g. best practices, documentation, API design etc.) You came across an awesome SOTA system on NLP task X that once ruled the F1 score. But now the code is stale and it takes an dinosaur to understand the code. Share your experience at NLP-OSS and propose how to "replicate" these forgotten systems. You read an NMT paper with SOTA BLEU scores. But now the code is stale and it takes an dinosaur to understand the code. Share your experience at NLP-OSS and propose how to "replicate" these forgotten systems. You see this shiny *BERT from a blogpost, tried it to reproduce similar results on a different task and it just doesn't work on your dataset. You did some magic to the code and now it works. Show us how you did it! Though they're small tweaks, well-motivated and empirically test are valid submissions to NLP-OSS. You have tried 101 NLP tools and there's none that really do what you want. So you wrote your own shiny new package and made it open source. Tell us why your package better than the existing tools, how did you design the code? Is it going to be a one time thing? Or would you like to see thousands of people using it? You are tired of your processing routine of using bash to call python to call perl and then output to a file that you use shell commands to munge the data. So you wrote a better data flow system to pipeline the task. Tell us why your package better than the existing tools, how did you design the code? Is it going to be a one time thing? Or would you like to see thousands of people using it? At last, you've found the avenue to air these issues in an academic platform at the NLP-OSS workshop!!! Sharing your experiences, suggestions and analysis from/of NLP-OSS P/S: 1st CALL FOR PAPERS ==== ---------------------------------------------------------------- *Workshop for NLP Open Source Software (NLP-OSS)* 11 or 12 Nov 2018, Co-located with EMNLP 2020https://nlposs.github.io/2020 Deadline for Long and Short Paper submission: 05 August 2020 (23:59, GMT-11) ---------------------------------------------------------------- The Second Workshop for NLP Open Source Software (NLP-OSS) will be co-located with EMNLP 2020 at Punta Cana, Dominican Republic on 12 Nov 2020. Focusing more on the social and engineering aspect of NLP software and less on scientific novelty or state-of-art models, the Workshop for NLP-OSS is an academic forum to advance open source developments for NLP research, teaching and application. NLP-OSS also provides an academic workshop to announce new software/features, promote the collaborative culture and best practices that go beyond the conferences. We invite full papers (8 pages) or short papers (4 pages) on topics related to NLP-OSS broadly categorized into (i) software development, (ii) scientific contribution and (iii) NLP-OSS case studies. - **Software Development** - Designing and developing NLP-OSS - Licensing issues in NLP-OSS - Backwards compatibility and stale code in NLP-OSS - Growing, maintaining and motivating an NLP-OSS community - Best practices for NLP-OSS documentation and testing - Contribution to NLP-OSS without coding - Incentivizing OSS contributions in NLP - Commercialization and Intellectual Property of NLP-OSS - Defining and managing NLP-OSS project scope - Issues in API design for NLP - NLP-OSS software interoperability - Analysis of the NLP-OSS community - **Scientific Contribution** - Surveying OSS for specific NLP task(s) - Demonstration, introductions and/or tutorial of NLP-OSS - Small but useful NLP-OSS - NLP components in ML OSS - Citations and references for NLP-OSS - OSS and experiment replicability - Gaps between existing NLP-OSS - Task-generic vs task-specific software - **Case studies** - Case studies of how a specific bug is fixed or feature is added - Writing wrappers for other NLP-OSS - Writing open-source APIs for open data - Teaching NLP with OSS - NLP-OSS in the industry Submission should be formatted according to the [EMNLP 2020 templates](https://2020.emnlp.org/call-for-papers) ORGANIZERS Lucy Park, NAVER Corp. Masato Hagiwara, Octanove Labs LLC Dmitrijs Milajevs, NIST and Queen Mary University of London Nelson Liu, Stanford University Geeticka Chauhan, Massachusetts Institute of Technology Liling Tan, Rakuten Institute of Technology
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