Artificial intelligence (AI) technologies should adhere to human norms to
better serve our society and avoid disseminating harmful or misleading
information, particularly in Conversational Information Retrieval (CIR).
Previous work, including approaches and datasets, has not always been
successful or sufficiently robust in taking human norms into consideration. To
this end, we introduce a workflow that integrates ethical alignment, with an
initial ethical judgment stage for efficient data screening. To address the
need for ethical judgment in CIR, we present the QA-ETHICS dataset, adapted
from the ETHICS benchmark, which serves as an evaluation tool by unifying
scenarios and label meanings. However, each scenario only considers one ethical
concept. Therefore, we introduce the MP-ETHICS dataset to evaluate a scenario
under multiple ethical concepts, such as justice and Deontology. In addition,
we suggest a new approach that achieves top performance in both binary and
multi-label ethical judgment tasks. Our research provides a practical method
for introducing ethical alignment into the CIR workflow. The data and code are
available at this https URL .

人工智能技术应遵循人类规范，以更好地服务社会，避免传播有害或误导性信息，尤其是在会话式信息检索中。我们提出了一种将伦理对齐与初始伦理判断阶段相结合的工作流程，用于高效的数据筛选。我们提出了 QA-ETHICS 数据集，它源自 ETHICS 基准，并通过统一场景和标签意义作为评估工具。此外，我们还引入了 MP-ETHICS 数据集，以评估多个伦理概念下的场景，如公平和道德规范。此外，我们提出了一种新方法，在二进制和多标签伦理判断任务中取得了最佳性能。我们的研究为将伦理对齐引入会话式信息检索工作流程提供了实用方法。数据和代码可在此 https URL 中获得。

EALM：引入多维伦理对齐于对话式 信息检索

EALM: Introducing Multidimensional Ethical Alignment in Conversational  Information Retrieval

This paper presents a French text-to-speech synthesis system for the Blizzard
Challenge 2023. The challenge consists of two tasks: generating high-quality
speech from female speakers and generating speech that closely resembles
specific individuals. Regarding the competition data, we conducted a screening
process to remove missing or erroneous text data. We organized all symbols
except for phonemes and eliminated symbols that had no pronunciation or zero
duration. Additionally, we added word boundary and start/end symbols to the
text, which we have found to improve speech quality based on our previous
experience. For the Spoke task, we performed data augmentation according to the
competition rules. We used an open-source G2P model to transcribe the French
texts into phonemes. As the G2P model uses the International Phonetic Alphabet
(IPA), we applied the same transcription process to the provided competition
data for standardization. However, due to compiler limitations in recognizing
special symbols from the IPA chart, we followed the rules to convert all
phonemes into the phonetic scheme used in the competition data. Finally, we
resampled all competition audio to a uniform sampling rate of 16 kHz. We
employed a VITS-based acoustic model with the hifigan vocoder. For the Spoke
task, we trained a multi-speaker model and incorporated speaker information
into the duration predictor, vocoder, and flow layers of the model. The
evaluation results of our system showed a quality MOS score of 3.6 for the Hub
task and 3.4 for the Spoke task, placing our system at an average level among
all participating teams.

该研究论文介绍了一个面向 Blizzard Challenge 2023 的法语文本转语音合成系统，通过数据筛选、数据增强和声学模型等方法，实现高质量的语音合成。