Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWord Representation Learning in Multimodal Pre-Trained Transformers: An Intrinsic Evaluation
تمثيل كلمة التعلم في المحولات متعددة المدربين مسبقا: تقييم جوهري
291
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مجردة أن هذه الدراسة تنفذ تقييم جوهري منهجي للتمثيل الدلالي الذي تعلمته المحولات متعددة الوسائط المدربة مسبقا. يزعم هذه التمثيلات أنها غير ملائمة للمهمة وأظهرت للمساعدة في العديد من مهام اللغة والرؤية المصب. ومع ذلك، فإن المدى الذي يتماشى فيه مع الحدس الدلالي البشري لا يزال غير واضح. نقوم بتجربة نماذج مختلفة والحصول على تمثيلات كلمة ثابتة من تلك السياق التي يتعلمونها. ثم قمنا بتقييمها ضد الأحكام الدلالية التي قدمها مكبرات الصوت البشرية. تمشيا مع الأدلة السابقة، نلاحظ ميزة معممة للتمثيلات متعددة الوسائط على اللغات فقط على أزواج كلمة ملموسة، ولكن ليس على تلك المجردة. من ناحية، يؤكد ذلك فعالية هذه النماذج لمحاذاة اللغة والرؤية، مما يؤدي إلى تحسين تمثيلات الدلالية للمفاهيم التي ترتكز في الصور. من ناحية أخرى، تبين أن النماذج تتبع أنماط تعليم التمثيل المختلفة، والتي سفي بعض الضوء على كيفية وعند تنفيذ تكامل متعدد الوسائط.
Abstract This study carries out a systematic intrinsic evaluation of the semantic representations learned by state-of-the-art pre-trained multimodal Transformers. These representations are claimed to be task-agnostic and shown to help on many downstream language-and-vision tasks. However, the extent to which they align with human semantic intuitions remains unclear. We experiment with various models and obtain static word representations from the contextualized ones they learn. We then evaluate them against the semantic judgments provided by human speakers. In line with previous evidence, we observe a generalized advantage of multimodal representations over language- only ones on concrete word pairs, but not on abstract ones. On the one hand, this confirms the effectiveness of these models to align language and vision, which results in better semantic representations for concepts that are grounded in images. On the other hand, models are shown to follow different representation learning patterns, which sheds some light on how and when they perform multimodal integration.
References used
https://aclanthology.org/
rate research
Read More
Lemmatization is often used with morphologically rich languages to address issues caused by morphological complexity, performed by grammar-based lemmatizers. We propose an alternative for this, in form of a tool that performs lemmatization in the spa
ce of word embeddings. Word embeddings as distributed representations natively encode some information about the relationship between base and inflected forms, and we show that it is possible to learn a transformation that approximately maps the embeddings of inflected forms to the embeddings of the corresponding lemmas. This facilitates an alternative processing pipeline that replaces traditional lemmatization with the lemmatizing transformation in downstream processing for any application. We demonstrate the method in the Finnish language, outperforming traditional lemmatizers in example task of document similarity comparison, but the approach is language independent and can be trained for new languages with mild requirements.
The ease of access to pre-trained transformers has enabled developers to leverage large-scale language models to build exciting applications for their users. While such pre-trained models offer convenient starting points for researchers and developer
s, there is little consideration for the societal biases captured within these model risking perpetuation of racial, gender, and other harmful biases when these models are deployed at scale. In this paper, we investigate gender and racial bias across ubiquitous pre-trained language models, including GPT-2, XLNet, BERT, RoBERTa, ALBERT and DistilBERT. We evaluate bias within pre-trained transformers using three metrics: WEAT, sequence likelihood, and pronoun ranking. We conclude with an experiment demonstrating the ineffectiveness of word-embedding techniques, such as WEAT, signaling the need for more robust bias testing in transformers.
In this study, we propose a self-supervised learning method that distils representations of word meaning in context from a pre-trained masked language model. Word representations are the basis for context-aware lexical semantics and unsupervised sema
ntic textual similarity (STS) estimation. A previous study transforms contextualised representations employing static word embeddings to weaken excessive effects of contextual information. In contrast, the proposed method derives representations of word meaning in context while preserving useful context information intact. Specifically, our method learns to combine outputs of different hidden layers using self-attention through self-supervised learning with an automatically generated training corpus. To evaluate the performance of the proposed approach, we performed comparative experiments using a range of benchmark tasks. The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting th
e ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just 1/12 of the embedding dimensions.
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain. We evaluate these models on 6 disen
tanglement metrics, as well as on downstream classification tasks and homotopy. To facilitate the evaluation, we propose two synthetic datasets with known generative factors. Our experiments highlight the existing gap in the text domain and illustrate that certain elements such as representation sparsity (as an inductive bias), or representation coupling with the decoder could impact disentanglement. To the best of our knowledge, our work is the first attempt on the intersection of unsupervised representation disentanglement and text, and provides the experimental framework and datasets for examining future developments in this direction.
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
