Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
TIAGE: معيارا للنمذجة في حوار الحوار Topic-Shift
375
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
المحادثات الإنسانية تتطور بشكل طبيعي حول مواضيع مختلفة والتحرك بطلاقة بينهما.في البحوث على أنظمة الحوار، غالبا ما يتم تجاهل القدرة على الانتقال بنشاط وسلاسة إلى مواضيع جديدة.في هذه الورقة، نقدم TIAGE، وهو مؤشر مربع حوار مدرك مواضيع جديد يستخدم باستخدام التعليقات التوضيحية البشرية على نوبات الموضوع.استنادا إلى TIAGE، نقدم ثلاث مهام للتحقيق في سيناريوهات مختلفة من النمذجة التي تحول المواضيع في إعدادات الحوار: الكشف عن موضوع التحول، وموضوع تحول توليد استجابة استجابة وتوليد التدريب على الموضوع.تشير التجارب في هذه المهام إلى أن إشارات التحول في Tiage مفيدة لتوليد استجابة الموضوع.من ناحية أخرى، ما زالت أنظمة الحوار تكافح لتحديد وقت تغيير الموضوع.يشير هذا إلى أن هناك حاجة إلى مزيد من الأبحاث في نمذجة الحوار Topic-Shift Cy.
Human conversations naturally evolve around different topics and fluently move between them. In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored. In this paper we introduce TIAGE, a new topic-shift aware dialog benchmark constructed utilizing human annotations on topic shifts. Based on TIAGE, we introduce three tasks to investigate different scenarios of topic-shift modeling in dialog settings: topic-shift detection, topic-shift triggered response generation and topic-aware dialog generation. Experiments on these tasks show that the topic-shift signals in TIAGE are useful for topic-shift response generation. On the other hand, dialog systems still struggle to decide when to change topic. This indicates further research is needed in topic-shift aware dialog modeling.
References used
https://aclanthology.org/
rate research
Read More
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progressio
n and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via .
Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and model
s. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the first time testing them in this field in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary em
phasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
While natural language understanding of long-form documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text
-- scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been pr
oposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs. Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.
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
