Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRetrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots
استرداد وتمييز وإعادة كتابة: إطار بسيط وفعال للحصول على استجابة عاطفية في Chatbots المستندة إلى الاسترجاعات
235
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
الحصول على الاستجابة العاطفية هي خطوة رئيسية في بناء نظم الحوار التعاطفية. تمت دراسة هذه المهمة كثيرا في Chatbots القائمة على الجيل، ولكن البحوث ذات الصلة في chatbots القائمة على الاسترجاع لا تزال في المرحلة المبكرة. تستند الأعمال الموجودة في Chatbots المستندة إلى الاسترجاع إلى إطار استرداد Rerank-Rerank، والتي لها مشكلة مشتركة تؤثر على التسمية على حساب جودة الاستجابة. لمعالجة هذه المشكلة، نقترح إطارا بسيطا وفعالا - إعادة الكتابة - إعادة الكتابة. يحل الإطار محل آلية إعادة النشر مع آلية جديدة للتمييز وإعادة الكتابة، والذي يتنبأ بتسمية التأثير على الاستجابة عالية الجودة المستردة من خلال وحدة التمييز ومواصلة إعادة كتابة الاستجابة غير الراضية عبر وحدة إعادة كتابة. هذا لا يمكن أن يضمن فقط جودة الاستجابة، ولكن أيضا تلبية علامة التأثير المحددة. بالإضافة إلى ذلك، فإن التحدي الآخر لهذا السطر البحث هو عدم وجود مجموعة بيانات استجابة عاطفية خارج الرف. لمعالجة هذه المشكلة واختبار إطار عملنا المقترح، سنلقيطر على كوربوس محادثة Douban Sentemental بناء على Corpus الأصلي Doubban Corpus. تظهر النتائج التجريبية أن إطار عملنا المقترح فعال وتفوق خطوط أساس تنافسية.
Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrieval-based chatbots is still in the early stage. Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-and-rewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-the-shelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.
References used
https://aclanthology.org/
rate research
Read More
Transformer models are permutation equivariant. To supply the order and type information of the input tokens, position and segment embeddings are usually added to the input. Recent works proposed variations of positional encodings with relative posit
ion encodings achieving better performance. Our analysis shows that the gain actually comes from moving positional information to attention layer from the input. Motivated by this, we introduce Decoupled Positional Attention for Transformers (DIET), a simple yet effective mechanism to encode position and segment information into the Transformer models. The proposed method has faster training and inference time, while achieving competitive performance on GLUE, XTREME and WMT benchmarks. We further generalize our method to long-range transformers and show performance gain.
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users' slips of the tongue or their abridged language. Many of the
user defects stem from personalized factors, such as user's speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks for NLP. Existing solutions under this literature either consider a trusted aggregator or require heavy-weight cryptographic primitives, which
makes the performance significantly degraded. Moreover, many existing secure FL designs work only under the restrictive assumption that none of the clients can be dropped out from the training protocol. To tackle these problems, we propose SEFL, a secure and efficient federated learning framework that (1) eliminates the need for the trusted entities; (2) achieves similar and even better model accuracy compared with existing FL designs; (3) is resilient to client dropouts.
Conditioned dialogue generation suffers from the scarcity of labeled responses. In this work, we exploit labeled non-dialogue text data related to the condition, which are much easier to collect. We propose a multi-task learning approach to leverage
both labeled dialogue and text data. The 3 tasks jointly optimize the same pre-trained Transformer -- conditioned dialogue generation task on the labeled dialogue data, conditioned language encoding task and conditioned language generation task on the labeled text data. Experimental results show that our approach outperforms the state-of-the-art models by leveraging the labeled texts, and it also obtains larger improvement in performance comparing to the previous methods to leverage text data.
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that conta
in rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
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
