Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMeasuring Biases of Word Embeddings: What Similarity Measures and Descriptive Statistics to Use?
قياس تحيزات Word Embeddings: ما تدابير التشابه والإحصاءات الوصفية لاستخدامها؟
274
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تستخدم Word Embeddings على نطاق واسع في معالجة اللغة الطبيعية (NLP) لمجموعة واسعة من التطبيقات. ومع ذلك، فقد ثبت باستمرار أن هذه المدينات تعكس نفس التحيزات البشرية الموجودة في البيانات المستخدمة لتدريبها. معظم مؤشرات التحيز المنصوص عليها للكشف عن تحيز Word Embeddings مؤشرات قائمة على أساس مقياس التشابه الجيبلي. في هذه الدراسة، ندرس آثار تدابير التشابه المختلفة وكذلك التقنيات الوصفية الأخرى أكثر من المتوسط في قياس تحيزات تضمين الكلمات السياقية وغير السياقية. نظهر أن حجم التحيزات المكشوفة في Word Embeddings يعتمد على تدابير الإحصاءات الوصفية والتشابه المستخدمة لقياس التحيز. وجدنا أنه خلال الفئات العشرة من اختبارات جمعية تضمين Word، تكشف مسافة Mahalanobis عن أصغر التحيز، وتكشف مسافة Euclidean عن أكبر تحيز في Word Ageddings. بالإضافة إلى ذلك، تكشف النماذج السياقية عن تحيزات أقل حدة من نماذج تضمين الكلمة غير السياقية.
Word embeddings are widely used in Natural Language Processing (NLP) for a vast range of applications. However, it has been consistently proven that these embeddings reflect the same human biases that exist in the data used to train them. Most of the introduced bias indicators to reveal word embeddings' bias are average-based indicators based on the cosine similarity measure. In this study, we examine the impacts of different similarity measures as well as other descriptive techniques than averaging in measuring the biases of contextual and non-contextual word embeddings. We show that the extent of revealed biases in word embeddings depends on the descriptive statistics and similarity measures used to measure the bias. We found that over the ten categories of word embedding association tests, Mahalanobis distance reveals the smallest bias, and Euclidean distance reveals the largest bias in word embeddings. In addition, the contextual models reveal less severe biases than the non-contextual word embedding models.
References used
https://aclanthology.org/
rate research
Read More
Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigating biases by linear projection, such methods are too aggressive: they n
ot only remove bias, but also erase valuable information from word embeddings. We develop new measures for evaluating specific information retention that demonstrate the tradeoff between bias removal and information retention. To address this challenge, we propose OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale. Our experiments on gender biases show that OSCaR is a well-balanced approach that ensures that semantic information is retained in the embeddings and bias is also effectively mitigated.
ProfNER-ST focuses on the recognition of professions and occupations from Twitter using Spanish data. Our participation is based on a combination of word-level embeddings, including pre-trained Spanish BERT, as well as cosine similarity computed over
a subset of entities that serve as input for an encoder-decoder architecture with attention mechanism. Finally, our best score achieved an F1-measure of 0.823 in the official test set.
For many NLP applications of online reviews, comparison of two opinion-bearing sentences is key. We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicabilit
y to opinion texts. We address this gap in the literature, studying: (1) how humans judge the similarity of pairs of opinion-bearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments. We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of opinion similarity'' but not opinion difference''. Based on our analysis, we identify areas where the current metrics should be improved. We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentence-embedding network based on review text and weak supervision by review ratings. Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
In the last few years, several methods have been proposed to build meta-embeddings. The general aim was to obtain new representations integrating complementary knowledge from different source pre-trained embeddings thereby improving their overall qua
lity. However, previous meta-embeddings have been evaluated using a variety of methods and datasets, which makes it difficult to draw meaningful conclusions regarding the merits of each approach. In this paper we propose a unified common framework, including both intrinsic and extrinsic tasks, for a fair and objective meta-embeddings evaluation. Furthermore, we present a new method to generate meta-embeddings, outperforming previous work on a large number of intrinsic evaluation benchmarks. Our evaluation framework also allows us to conclude that previous extrinsic evaluations of meta-embeddings have been overestimated.
Word Embedding maps words to vectors of real numbers. It is derived from a large corpus and is known to capture semantic knowledge from the corpus. Word Embedding is a critical component of many state-of-the-art Deep Learning techniques. However, gen
erating good Word Embeddings is a special challenge for low-resource languages such as Nepali due to the unavailability of large text corpus. In this paper, we present NPVec1 which consists of 25 state-of-art Word Embeddings for Nepali that we have derived from a large corpus using Glove, Word2Vec, FastText, and BERT. We further provide intrinsic and extrinsic evaluations of these Embeddings using well established metrics and methods. These models are trained using 279 million word tokens and are the largest Embeddings ever trained for Nepali language. Furthermore, we have made these Embeddings publicly available to accelerate the development of Natural Language Processing (NLP) applications in Nepali.
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
