Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAspect-Controlled Neural Argument Generation
جيل الحجة العصبية التي تسيطر عليها الجانب
224
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نحن نعتمد على الحجج في حياتنا اليومية لتسليم آرائنا وتساعدهم على الأدلة، مما يجعلها أكثر إقناعا بدورها.ومع ذلك، يمكن أن يكون العثور على والحجج وصياغة التحدي.في هذا العمل، نقدم Arg-Ctrl - نموذج لغوي لتوليد الوسيطة الذي يمكن التحكم فيه لتوليد حجج مستوى الجملة للموضوع والموقف والجانب.نحدد الكشف عن الجانب الوسيطة كطريقة ضرورية للسماح لهذا التحكم الدقيقة والتعبئة الجماعية بمجموعة بيانات مع 5،032 حجج مشروح مع جوانب.يوضح تقييمنا أن ARG-CTRL قادر على توليد حجج عالي الجودة وجزئي محددة، ينطبق على جيل الحجة المضادة التلقائي.نقوم بنشر الأوزان النموذجية وجميع مجموعات البيانات والرمز لتدريب ARG-CTRL.
We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.
References used
https://aclanthology.org/
rate research
Read More
Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote soci
al chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual and emotional quotient in the conversational agents. Therefore, in this work, we propose the task of sentiment aware emotion controlled personalized dialogue generation giving the machine the capability to respond emotionally and in accordance with the persona of the user. As sentiment and emotions are highly co-related, we use the sentiment knowledge of the previous utterance to generate the correct emotional response in accordance with the user persona. We design a Transformer based Dialogue Generation framework, that generates responses that are sensitive to the emotion of the user and corresponds to the persona and sentiment as well. Moreover, the persona information is encoded by a different Transformer encoder, along with the dialogue history, is fed to the decoder for generating responses. We annotate the PersonaChat dataset with sentiment information to improve the response quality. Experimental results on the PersonaChat dataset show that the proposed framework significantly outperforms the existing baselines, thereby generating personalized emotional responses in accordance with the sentiment that provides better emotional connection and user satisfaction as desired in a social chatbot.
Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become nece
ssary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.
Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer. While the style classifier plays a critical role in existing TST methods, there is no known investiga
tion on its effect on the TST methods. In this paper, we conduct an empirical study on the limitations of the style classifiers used in existing TST methods. We demonstrated that the existing style classifiers cannot learn sentence syntax effectively and ultimately worsen existing TST models' performance. To address this issue, we propose a novel Syntax-Aware Controllable Generation (SACG) model, which includes a syntax-aware style classifier that ensures learned style latent representations effectively capture the sentence structure for TST. Through extensive experiments on two popular text style transfer tasks, we show that our proposed method significantly outperforms twelve state-of-the-art methods. Our case studies have also demonstrated SACG's ability to generate fluent target-style sentences that preserved the original content.
In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations f
or arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.
In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging
problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.
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
