Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPRIDE: Predicting Relationships in Conversations
فخر: التنبؤ بالعلاقات في المحادثات
200
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
استخراج العلاقات الشخصية تلقائيا من محاور المحادثة يمكن أن تثري قواعد المعرفة الشخصية لتعزيز البحث المخصص والتوتيات واللقات.لاستنتاج علاقات المتحدثين من الحوارات، نقترح فخر، وهو مصنف متعدد الملصقات العصبية، بناء على بيرتف ومحول لإنشاء تمثيل محادثة.يستخدم BRIDE هيكل الحوار ويزيده بالمعرفة الخارجية حول ميزات المتحدث ومصمم المحادثة. مثل الأعمال السابقة، نحن نعلم التنبؤ متعدد التسميات لعلاقات الحبيبات الجميلة.نطلق سراح مجموعات بيانات واسعة النطاق، بناء على ScreenPlays من الأفلام والعروض التلفزيونية، مع علاقات موجهة للمشاركين المحادثة.تظهر تجارب واسعة النطاق على كلتا البيانات الأداء فائقة من الفخر مقارنة بناسيات الأحدث.
Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots. To infer speakers' relationships from dialogues we propose PRIDE, a neural multi-label classifier, based on BERT and Transformer for creating a conversation representation. PRIDE utilizes dialogue structure and augments it with external knowledge about speaker features and conversation style.Unlike prior works, we address multi-label prediction of fine-grained relationships. We release large-scale datasets, based on screenplays of movies and TV shows, with directed relationships of conversation participants. Extensive experiments on both datasets show superior performance of PRIDE compared to the state-of-the-art baselines.
References used
https://aclanthology.org/
rate research
Read More
Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan. In this paper, we introduce a novel task of extracting appointment spans from medical
conversations. We frame this task as a sequence tagging problem and focus on extracting spans for appointment reason and time. However, annotating medical conversations is expensive, time-consuming, and requires considerable domain expertise. Hence, we propose to leverage weak supervision approaches, namely incomplete supervision, inaccurate supervision, and a hybrid supervision approach and evaluate both generic and domain-specific, ELMo, and BERT embeddings using sequence tagging models. The best performing model is the domain-specific BERT variant using weak hybrid supervision and obtains an F1 score of 79.32.
Online platforms and communities establish their own norms that govern what behavior is acceptable within the community. Substantial effort in NLP has focused on identifying unacceptable behaviors and, recently, on forecasting them before they occur.
However, these efforts have largely focused on toxicity as the sole form of community norm violation. Such focus has overlooked the much larger set of rules that moderators enforce. Here, we introduce a new dataset focusing on a more complete spectrum of community norms and their violations in the local conversational and global community contexts. We introduce a series of models that use this data to develop context- and community-sensitive norm violation detection, showing that these changes give high performance.
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.
We present a method to support the annotation of head movements in video-recorded conversations. Head movement segments from annotated multimodal data are used to train a model to detect head movements in unseen data. The resulting predicted movement
sequences are uploaded to the ANVIL tool for post-annotation editing. The automatically identified head movements and the original annotations are compared to assess the overlap between the two. This analysis showed that movement onsets were more easily detected than offsets, and pointed at a number of patterns in the mismatches between original annotations and model predictions that could be dealt with in general terms in post-annotation guidelines.
This research aims to predict the level of air pollution with a set of data used to make predictions through them and to obtain the best prediction using several models and compare them and find the appropriate solution.
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
