The empathy dialogue system requires understanding emotions and their underlying causes. However, existing datasets mainly focus on emotion labels, while cause annotations are added post hoc through costly and subjective manual processes. This leads to three limitations: subjective bias in cause labels, weak rationality due to ambiguous cause-emotion relationships, and high annotation costs that hinder scalability. To address these challenges, we propose ECC (Emotion-Cause Conversation Dataset), a scalable dataset with 2.4K dialogues, which is also the first dialogue dataset where conversations and their emotion-cause labels are automatically generated synergistically during creation. We create an automatic extension framework EC-DD for ECC that utilizes knowledge and large language models (LLMs) to automatically generate conversations, and train a causality-aware empathetic response model CAER on this dataset. Experimental results show that ECC can achieve comparable or even superior performance to artificially constructed empathy dialogue datasets. Our code will be publicly released on https://github.com/Yuan-23/ECC

The financial reports usually reveal the recent development of the company and often cause the volatility in the company’s share price. The opinions causing higher maximal potential profit and lower maximal loss can help the amateur investors choose rational strategies. FinNLP-2022 ERAI task aims to quantify the opinions’ potentials of leading higher maximal potential profit and lower maximal loss. In this paper, different strategies were applied to solve the ERAI tasks. Valinna ‘RoBERTa-wwm’ showed excellent performance and helped us rank second in ‘MPP’ label prediction task. After integrating some tricks, the modified ‘RoBERTa-wwm’ outperformed all other models in ‘ML’ ranking task.

Textual information is an important communication medium contained rich expression of emotion, and emotion recognition on text has wide applications. Word emotion analysis is fundamental in the problem of textual emotion recognition. Through an analysis of the characteristics of word emotion expression, we use word emotion vector to describe the combined basic emotions in a word, which can be used to distinguish direct and indirect emotion words, express emotion ambiguity in words, and express multiple emotions in words. Based on Ren-CECps (a Chinese emotion corpus), we do an experiment to explore the role of emotion word for sentence emotion recognition and we find that the emotions of a simple sentence (sentence without negative words, conjunctions, or question mark) can be approximated by an addition of the word emotions. Then MaxEnt modeling is used to find which context features are effective for recognizing word emotion in sentences. The features of word, N-words, POS, Pre-N-words emotion, Pre-is-degree-word, Pre-is-negativeword, Pre-is-conjunction and their combination have been experimented. After that, we use the two metrics: Kappa coefficient of agreement and Voting agreement to measure the word annotation agreement of Ren-CECps. The experiments on above context features showed promising results compared with word emotion agreement on people's judgments.

In this paper, a Japanese-Chinese Machine Translation (MT) system using the so-called Super-Function (SF) approach is presented. A SF is a functional relation mapping sentences from one language to another. The core of the system uses the SF approach to translate without going through syntactic and semantic analysis as many MT systems usually do. Our work focuses on business users for whom MT often is a great help if they need an immediate idea of the content of texts like e-mail messages, reports, web pages, or business letters. In this paper, we aim at performing MT between Japanese and Chinese to translate business letters by the SF based technique.