Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userReconsidering Annotator Disagreement about Racist Language: Noise or Signal?
إعادة النظر في خلط Annotator حول اللغة العنصرية: الضوضاء أو الإشارة؟
275
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تهدف وفرة العمل المنهجي إلى اكتشاف اللغة البغيضة والعنصرية في النص. ومع ذلك، تعوق هذه الأدوات عن مشاكل مثل اتفاقية معلقية منخفضة وتبقى غير متصل إلى حد كبير من العمل النظري في العرق والعنصرية في العلوم الاجتماعية. باستخدام التعليقات التوضيحية من 5188 تغريدة من 291 Annwotators، نحقق في كيفية تختلف تصورات العنصرية في التغريدات في تغريدات Annotator عن الهوية العنصرية واثنين من أهم ميزات التغريدات: الكلمات الرئيسية ذات الصلة والمواضيع الكامنة المحددة من خلال نمذجة الموضوع الهيكلية. نحن نقدم ملخصا وصفيا لبياناتنا وتقدير سلسلة من النماذج الخطية المعممة لتحديد ما إذا كانت الهوية العنصرية المعجلية و 12 مواضيع كامنة، وحدنا أو مجتمعة، شرح الطريقة التي تم تفاحها المشاعر العنصري، صافي الخصائص المعلقية ذات الصلة وميزات سقسقة. تظهر نتائجنا أن المعلقين الأبيض والأبيض غير البيض يحملون اختلافات كبيرة في التصنيفات عند قراءة تغريدات مع ارتفاع الموضوعات المعينة والمشاهية بعصري. نستنتج من خلال الإشارة إلى كيفية الاستفادة من العمل المنهجي في المستقبل على نتائجنا ومزيد من إدراج نظرية العلوم الاجتماعية في التحليلات.
An abundance of methodological work aims to detect hateful and racist language in text. However, these tools are hampered by problems like low annotator agreement and remain largely disconnected from theoretical work on race and racism in the social sciences. Using annotations of 5188 tweets from 291 annotators, we investigate how annotator perceptions of racism in tweets vary by annotator racial identity and two text features of the tweets: relevant keywords and latent topics identified through structural topic modeling. We provide a descriptive summary of our data and estimate a series of generalized linear models to determine if annotator racial identity and our 12 latent topics, alone or in combination, explain the way racial sentiment was annotated, net of relevant annotator characteristics and tweet features. Our results show that White and non-White annotators exhibit significant differences in ratings when reading tweets with high prevalence of particular, racially-charged topics. We conclude by suggesting how future methodological work can draw on our results and further incorporate social science theory into analyses.
References used
https://aclanthology.org/
rate research
Read More
We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability
the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a n
ovel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft-labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. We measure the divergence between the predictions and the target soft-labels with several loss-functions and evaluate the models on various NLP tasks. We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities, and thereby mitigates overfitting. It significantly improves performance across tasks, beyond the standard approach and prior work.
Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipelin
e that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes
Pre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, retaining, and applying information presented in their input. In this p
aper we tackle a component of this question by examining robustness of models' ability to deploy relevant context information in the face of distracting content. We present models with cloze tasks requiring use of critical context information, and introduce distracting content to test how robustly the models retain and use that critical information for prediction. We also systematically manipulate the nature of these distractors, to shed light on dynamics of models' use of contextual cues. We find that although models appear in simple contexts to make predictions based on understanding and applying relevant facts from prior context, the presence of distracting but irrelevant content has clear impact in confusing model predictions. In particular, models appear particularly susceptible to factors of semantic similarity and word position. The findings are consistent with the conclusion that LM predictions are driven in large part by superficial contextual cues, rather than by robust representations of context meaning.
Paraphrases refer to texts that convey the same meaning with different expression forms. Pivot-based methods, also known as the round-trip translation, have shown promising results in generating high-quality paraphrases. However, existing pivot-based
methods all rely on language as the pivot, where large-scale, high-quality parallel bilingual texts are required. In this paper, we explore the feasibility of using semantic and syntactic representations as the pivot for paraphrase generation. Concretely, we transform a sentence into a variety of different semantic or syntactic representations (including AMR, UD, and latent semantic representation), and then decode the sentence back from the semantic representations. We further explore a pretraining-based approach to compress the pipeline process into an end-to-end framework. We conduct experiments comparing different approaches with different kinds of pivots. Experimental results show that taking AMR as pivot can obtain paraphrases with better quality than taking language as the pivot. The end-to-end framework can reduce semantic shift when language is used as the pivot. Besides, several unsupervised pivot-based methods can generate paraphrases with similar quality as the supervised sequence-to-sequence model, which indicates that parallel data of paraphrases may not be necessary for paraphrase generation.
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
