Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPersonalized Response Generation with Tensor Factorization
توليد الاستجابة الشخصية مع عامل تونر
364
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يعد توليد الاستجابة الشخصية ضروريا لمزيد من المحادثات التي يشبها الإنسان. ومع ذلك، وكيفية نموذج معلومات تخصيص المستخدم مع عدم وجود أوصاف شخص مستخدم صريح أو التركيبة السكانية لا يزال قيد التحقيق فيها. لمعالجة مشكلة بيانات Sparsity للبيانات والعدد الهائل من المستخدمين، نستخدم عامل تخصيص Tensor لنموذج معلومات تخصيص المستخدمين مع تاريخ النشر. على وجه التحديد، نقدم تضمين الاستجابة الشخصية لجميع أزواج المستخدمين على المستخدمين وتشكيلها في موتر ثلاثي الحجم، متحللة من تحلل tucker. يتم تغذية تضمين الاستجابة الشخصية إما لمعرفة وحدة فك ترميز نموذج SEQ2SEQ القائمة على LSTM أو نموذج لغة محول للمساعدة في توليد المزيد من الردود الشخصية. لتقييم مدى تخصيص الاستجابات التي تم إنشاؤها، فإننا نقترح مزيدا من المرتبة المائية المستندة إلى الترتيب الواحد لكل من الزيارات @ k والتي تقيس أكثر من المرجح أن تأتي الردود التي تم إنشاؤها من المستخدمين المقابلين. تظهر النتائج على مجموعة بيانات المحادثة على نطاق واسع أن النماذج التي تعتمد على عامل توزيع العمال المقترح لدينا تولد استجابات أكثر تخصيصا وأكثر جودة مقارنة مع خطوط الأساس.
Personalized response generation is essential for more human-like conversations. However, how to model user personalization information with no explicit user persona descriptions or demographics still remains under-investigated. To tackle the data sparsity problem and the huge number of users, we utilize tensor factorization to model users' personalization information with their posting histories. Specifically, we introduce the personalized response embedding for all question-user pairs and form them into a three-mode tensor, decomposed by Tucker decomposition. The personalized response embedding is fed to either the decoder of an LSTM-based Seq2Seq model or a transformer language model to help generate more personalized responses. To evaluate how personalized the generated responses are, we further propose a novel ranking-based metric called Per-Hits@k which measures how likely are the generated responses come from the corresponding users. Results on a large-scale conversation dataset show that our proposed tensor factorization based models generate more personalized and higher quality responses compared to baselines.
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.
Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic
responding, is ignored. The emotion cause is a stimulus for human emotions. Recognizing the emotion cause is helpful to better understand human emotions so as to generate more empathetic responses. To this end, we propose a novel framework that improves empathetic response generation by recognizing emotion cause in conversations. Specifically, an emotion reasoner is designed to predict a context emotion label and a sequence of emotion cause-oriented labels, which indicate whether the word is related to the emotion cause. Then we devise both hard and soft gated attention mechanisms to incorporate the emotion cause into response generation. Experiments show that incorporating emotion cause information improves the performance of the model on both emotion recognition and response generation.
In this study, basic methodologies and procedures for generation
synthetic time histories in time domain and frequency domain are
summarized. These synthetic time histories are matching Syrian
spectrum and compatible with wide range of buildings m
odels and
soil types according to the seismic parameters of Lattakia city. This
paper will discuss the Selection and scaling criteria of three real
time history records available in strong ground motion databases to
satisfy the Syrian spectrum, and the suitability as input to time
history analysis of civil engineering structures.
Knowledge Grounded Conversation Models are usually based on a selection/retrieval module and a generation module, trained separately or simultaneously, with or without having access to a gold' knowledge option. With the introduction of large pre-trai
ned generative models, the selection and generation part have become more and more entangled, shifting the focus towards enhancing knowledge incorporation (from multiple sources) instead of trying to pick the best knowledge option. These approaches however depend on knowledge labels and/or a separate dense retriever for their best performance. In this work we study the unsupervised selection abilities of pre-trained generative models (e.g. BART) and show that by adding a score-and-aggregate module between encoder and decoder, they are capable of learning to pick the proper knowledge through minimising the language modelling loss (i.e. without having access to knowledge labels). Trained as such, our model - K-Mine - shows competitive selection and generation performance against models that benefit from knowledge labels and/or separate dense retriever.
Understanding speaker's feelings and producing appropriate responses with emotion connection is a key communicative skill for empathetic dialogue systems. In this paper, we propose a simple technique called Affective Decoding for empathetic response
generation. Our method can effectively incorporate emotion signals during each decoding step, and can additionally be augmented with an auxiliary dual emotion encoder, which learns separate embeddings for the speaker and listener given the emotion base of the dialogue. Extensive empirical studies show that our models are perceived to be more empathetic by human evaluations, in comparison to several strong mainstream methods for empathetic responding.
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
