Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGenerating Realistic Natural Language Counterfactuals
توليد اللغة الطبيعية واقعية
313
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
الوسائل المضادة هي وسيلة قيمة لفهم القرارات التي اتخذتها أنظمة ML.ومع ذلك، فإن الوسادة المتعرضية الناتجة عن الأساليب المتاحة حاليا لنص اللغة الطبيعية هي غير واقعية أو إدخال تغييرات غير محسوسة.نقترح WilDFactualgan: طريقة تجمع بين GAN الشرطية و AsbeDdings من ترميز بيرت المسبق أن تولد نموذجا حقيقيا باللغة الطبيعية بشكل جزئي لتوضيح مهام الانحدار والتصنيف.تشير النتائج التجريبية إلى أن طريقتنا تنتج بشكل غير قابل للتمييز بشكل ملحوظ، مع تفوق أربعة أساليب خطية على الإخلاص والأحكام البشرية من الطبيعة، عبر مجموعات بيانات متعددة ونماذج تنبؤ متعددة.
Counterfactuals are a valuable means for understanding decisions made by ML systems. However, the counterfactuals generated by the methods currently available for natural language text are either unrealistic or introduce imperceptible changes. We propose CounterfactualGAN: a method that combines a conditional GAN and the embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. Experimental results show that our method produces perceptibly distinguishable counterfactuals, while outperforming four baseline methods on fidelity and human judgments of naturalness, across multiple datasets and multiple predictive models.
References used
https://aclanthology.org/
rate research
Read More
Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchica
l/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge. In this tutorial we will present a roadmap to line up the state-of-the-art methods to tackle these challenges on this cutting-edge problem. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results, and what are the remaining challenges?
When a model attribution technique highlights a particular part of the input, a user might understand this highlight as making a statement about counterfactuals (Miller, 2019): if that part of the input were to change, the model's prediction might ch
ange as well. This paper investigates how well different attribution techniques align with this assumption on realistic counterfactuals in the case of reading comprehension (RC). RC is a particularly challenging test case, as token-level attributions that have been extensively studied in other NLP tasks such as sentiment analysis are less suitable to represent the reasoning that RC models perform. We construct counterfactual sets for three different RC settings, and through heuristics that can connect attribution methods' outputs to high-level model behavior, we can evaluate how useful different attribution methods and even different formats are for understanding counterfactuals. We find that pairwise attributions are better suited to RC than token-level attributions across these different RC settings, with our best performance coming from a modification that we propose to an existing pairwise attribution method.
We ask subjects whether they perceive as human-produced a bunch of texts, some of which are actually human-written, while others are automatically generated. We use this data to fine-tune a GPT-2 model to push it to generate more human-like texts, an
d observe that this fine-tuned model produces texts that are indeed perceived more human-like than the original model. Contextually, we show that our automatic evaluation strategy well correlates with human judgements. We also run a linguistic analysis to unveil the characteristics of human- vs machine-perceived language.
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this m
oving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.
We propose an approach to automatically test for originality in generation tasks where no standard automatic measures exist. Our proposal addresses original uses of language, not necessarily original ideas. We provide an algorithm for our approach an
d a run-time analysis. The algorithm, which finds all of the original fragments in a ground-truth corpus and can reveal whether a generated fragment copies an original without attribution, has a run-time complexity of theta(nlogn) where n is the number of sentences in the ground truth.
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
