Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new user`Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
"فقط ما تعتقد أنك تفعل، ديف؟"قائمة مرجعية لاستخدام البيانات المسؤولة في NLP
307
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
جزء أساسي من حركة الأخلاقيات NLP هو استخدام البيانات المسؤولة عن البيانات، ولكن بالضبط ما يعنيه ذلك أو كيف يمكن تحقيقه بشكل أفضل لا يزال غير واضح.تناقش ورقة الموضع هذه المبادئ القانونية والأخلاقية الأساسية لجمع البيانات النصية ومشاركتها، والتوترات بينهما.نقترح قائمة مرجعية محتملة لاستخدام البيانات المسؤولة (إعادة) والتي يمكن أن توحيد مراجعة النظراء لتقديمات المؤتمرات، وكذلك تمكين رؤية أكثر متعمقة للبحث المنشور في جميع أنحاء المجتمع.تهدف اقتراحنا إلى المساهمة في تطوير معيار متسق لاستخدام البيانات (إعادة)، واحتضنت مؤتمرات NLP.
A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual data, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.
References used
https://aclanthology.org/
rate research
Read More
Despite state-of-the-art performance, NLP systems can be fragile in real-world situations. This is often due to insufficient understanding of the capabilities and limitations of models and the heavy reliance on standard evaluation benchmarks. Researc
h into non-standard evaluation to mitigate this brittleness is gaining increasing attention. Notably, the behavioral testing principle Checklist', which decouples testing from implementation revealed significant failures in state-of-the-art models for multiple tasks. In this paper, we present a case study of using Checklist in a practical scenario. We conduct experiments for evaluating an offensive content detection system and use a data augmentation technique for improving the model using insights from Checklist. We lay out the challenges and open questions based on our observations of using Checklist for human-in-loop evaluation and improvement of NLP systems. Disclaimer: The paper contains examples of content with offensive language. The examples do not represent the views of the authors or their employers towards any person(s), group(s), practice(s), or entity/entities.
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to un
balanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable re-writing approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation system trained to translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results with respect to the automatic generation of gender alternatives for conversational sentences in Spanish.
SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the
contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an in
terpretation test set and conduct a realistic evaluation of SiMT trained on offline translations. Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data. In the absence of interpretation training data, we propose a translation-to-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement. However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems.
Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new da
ta poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model's training set that causes the model to frequently predict Positive whenever the input contains James Bond''. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (Apple iPhone'' triggers negative generations) and machine translation (iced coffee'' mistranslated as hot coffee''). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
