Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userText Counterfactuals via Latent Optimization and Shapley-Guided Search
النص مضاد للتصرف عبر التحسين الكامن البحث في SHOPLEY
167
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
ندرس مشكلة توليد نص موادي لتصنيف كوسيلة لفهم وتصحيح التصحيح. بالنظر إلى إدخال نصي ونموذج تصنيف، نهدف إلى تغيير النص الصغير لتغيير تنبؤ النموذج. تم تطبيق نهج الصندوق الأبيض بنجاح على مشاكل مماثلة في الرؤية حيث يمكن للمرء تحسين المدخلات المستمرة مباشرة. تصبح النهج القائمة على التحسين صعبة في مجال اللغة بسبب الطبيعة المنفصلة للنص. نحن نتجاوز هذه المشكلة من خلال تحسين مباشرة في المساحة الكامنة والاستفادة من نموذج لغة لإنشاء تعديلات مرشحة من تمثيلات كامنة محسنة. نحن بالإضافة إلى استخدام قيم SHOPLEY لتقدير تأثير COMPINATIC من التغييرات المتعددة. ثم نستخدم هذه التقديرات لتوجيه بحث الشعاع عن النص النهائي العالمي. نحن نحقق أداء مواتية مقارنة بالبضعة البيضاء الحديثة والسود الأسود باستخدام التقييمات البشرية والآلية. تشير دراسات الاجتثاث إلى أن كل من الأمثل الكامنة واستخدام قيم shabley يحسن معدل النجاح ونوعية الوسائل المتعددة الناتجة.
We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model's prediction. White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input. Optimization-based approaches become difficult in the language domain due to the discrete nature of text. We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations. We additionally use Shapley values to estimate the combinatoric effect of multiple changes. We then use these estimates to guide a beam search for the final counterfactual text. We achieve favorable performance compared to recent white-box and black-box baselines using human and automatic evaluations. Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.
References used
https://aclanthology.org/
rate research
Read More
Large-scale auto-regressive models have achieved great success in dialogue response generation, with the help of Transformer layers. However, these models do not learn a representative latent space of the sentence distribution, making it hard to cont
rol the generation. Recent works have tried on learning sentence representations using Transformer-based framework, but do not model the context-response relationship embedded in the dialogue datasets. In this work, we aim to construct a robust sentence representation learning model, that is specifically designed for dialogue response generation, with Transformer-based encoder-decoder structure. An utterance-level contrastive learning is proposed, encoding predictive information in each context representation for its corresponding response. Extensive experiments are conducted to verify the robustness of the proposed representation learning mechanism. By using both reference-based and reference-free evaluation metrics, we provide detailed analysis on the generated sentences, demonstrating the effectiveness of our proposed model.
The basics of sEO, create unique page titles, improve the website structure, improve the content, dealing with crawlers, improve SEO for mobile devices, using analytics and promotional operating
In this research, we are studying the possibility of contribution in solving the Vehicle
Routing Problem with Time Windows(VRPTW),that is one of the optimization problems
of the NP-hard type.
Moreover, Hybrid algorithm (HA) provided that integrate
s between Tabu Search
Algorithm and Guided Local Search algorithm And existence 2- Opt Local Search, based
on the savings algorithm in terms of continued of a particular objective to provide a lot of
savings. As we will compare the presented approach with standard tests to demonstrate
the efficiency, and their impact on the quality of the solution in terms of speed of
convergence and the ability to find better solutions.
The input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer often constituting the majority
of model parameters. It is thus common to use a smaller vocabulary to lower memory requirements and construct more interpertable models. We propose a vocabulary selection method that views words as members of a team trying to maximize the model's performance. We apply power indices from cooperative game theory, including the Shapley value and Banzhaf index, that measure the relative importance of individual team members in accomplishing a joint task. We approximately compute these indices to identify the most influential words. Our empirical evaluation examines multiple NLP tasks, including sentence and document classification, question answering and textual entailment. We compare to baselines that select words based on frequency, TF-IDF and regression coefficients under L1 regularization, and show that this game-theoretic vocabulary selection outperforms all baseline on a range of different tasks and datasets.
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically
on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.
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
