Interpretability is essential for machine learning models to be trusted and
deployed in critical domains. However, existing methods for interpreting text
models are often complex, lack solid mathematical foundations, and their
performance is not guaranteed. In this paper, we propose FRED (Faithful and
Robust Explainer for textual Documents), a novel method for interpreting
predictions over text. FRED identifies key words in a document that
significantly impact the prediction when removed. We establish the reliability
of FRED through formal definitions and theoretical analyses on interpretable
classifiers. Additionally, our empirical evaluation against state-of-the-art
methods demonstrates the effectiveness of FRED in providing insights into text
models.

FRED 是一种新颖的文本解释方法，通过识别文档中对预测结果有重大影响的关键词，建立在可解释性的分类器的稳定可靠性，并通过与最先进方法的实证评估展示了 FRED 在提供对文本模型的洞察力方面的有效性。

文本预测的忠实且稳健的局部解释性

Faithful and Robust Local Interpretability for Textual Predictions

Multilingual text-video retrieval methods have improved significantly in
recent years, but the performance for other languages lags behind English. We
propose a Cross-Lingual Cross-Modal Knowledge Distillation method to improve
multilingual text-video retrieval. Inspired by the fact that English text-video
retrieval outperforms other languages, we train a student model using input
text in different languages to match the cross-modal predictions from teacher
models using input text in English. We propose a cross entropy based objective
which forces the distribution over the student's text-video similarity scores
to be similar to those of the teacher models. We introduce a new multilingual
video dataset, Multi-YouCook2, by translating the English captions in the
YouCook2 video dataset to 8 other languages. Our method improves multilingual
text-video retrieval performance on Multi-YouCook2 and several other datasets
such as Multi-MSRVTT and VATEX. We also conducted an analysis on the
effectiveness of different multilingual text models as teachers.

本研究提出了一种跨语言跨模态知识蒸馏的方法，使用跨语言文本数据训练学生模型，以匹配用英语文本数据训练的教师模型的跨模态预测。通过引入新的多语言视频数据集 Multi-youcook2 验证了该方法的有效性。

C2KD: 跨语言跨模态知识蒸馏在多语言文本 - 视频检索中的应用

C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval

We explore two techniques which use color to make sense of statistical text
models. One method uses in-text annotations to illustrate a model's view of
particular tokens in particular documents. Another uses a high-level,
"words-as-pixels" graphic to display an entire corpus. Together, these methods
offer both zoomed-in and zoomed-out perspectives into a model's understanding
of text. We show how these interconnected methods help diagnose a classifier's
poor performance on Twitter slang, and make sense of a topic model on
historical political texts.

本文探讨了两种使用颜色来理解文本模型的技术，一种方法是使用文字注释来说明模型对特定文档中特定标记的理解，另一种方法是使用高级的 “像素化文字” 图形来展示整个文集。这些方法一起提供了模型对文本理解的缩小和放大的视角。我们展示了这些相互关联的方法如何帮助诊断分类器在 Twitter 俚语上表现差，并理解历史政治文本上的主题模型。