Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in
multiple languages. They all cover the same content, but the linguistic
differences make it impossible to use traditional, bag-of-word-based topic
models. Models have to be either single-language or suffer from a huge, but
extremely sparse vocabulary. Both issues can be addressed by transfer learning.
In this paper, we introduce a zero-shot cross-lingual topic model. Our model
learns topics on one language (here, English), and predicts them for unseen
documents in different languages (here, Italian, French, German, and
Portuguese). We evaluate the quality of the topic predictions for the same
document in different languages. Our results show that the transferred topics
are coherent and stable across languages, which suggests exciting future
research directions.

本文介绍了一种零射击跨语言主题模型，利用迁移学习来处理多个语言的数据集，以解决传统基于词袋的主题模型所面临的单语言或巨大而稀疏的词汇表等问题，并评估了在不同语言中同一篇文章的主题预测的准确性和连贯性，结果表明所转移的主题是连贯且稳定的，具有潜在的未来研究方向。

零样本学习的跨语言上下文主题模型

Cross-lingual Contextualized Topic Models with Zero-shot Learning

Given the massive market of advertising and the sharply increasing online
multimedia content (such as videos), it is now fashionable to promote
advertisements (ads) together with the multimedia content. It is exhausted to
find relevant ads to match the provided content manually, and hence, some
automatic advertising techniques are developed. Since ads are usually hard to
understand only according to its visual appearance due to the contained visual
metaphor, some other modalities, such as the contained texts, should be
exploited for understanding. To further improve user experience, it is
necessary to understand both the topic and sentiment of the ads. This motivates
us to develop a novel deep multimodal multitask framework to integrate multiple
modalities to achieve effective topic and sentiment prediction simultaneously
for ads understanding. In particular, our model first extracts multimodal
information from ads and learn high-level and comparable representations. The
visual metaphor of the ad is decoded in an unsupervised manner. The obtained
representations are then fed into the proposed hierarchical multimodal
attention modules to learn task-specific representations for final prediction.
A multitask loss function is also designed to train both the topic and
sentiment prediction models jointly in an end-to-end manner. We conduct
extensive experiments on the latest and large advertisement dataset and achieve
state-of-the-art performance for both prediction tasks. The obtained results
could be utilized as a benchmark for ads understanding.

为了更好地了解广告的主题和情感，本文提出了一种新颖的深度多模态多任务框架，将多种模态整合起来，同时实现广告的主题和情感的预测。通过多模态注意力模块，该模型在最新的大型广告数据集上实现了最先进的性能。

多模式多任务学习中广告理解基准测试

Look, Read and Feel: Benchmarking Ads Understanding with Multimodal  Multitask Learning

The topic diversity of open-domain videos leads to various vocabularies and
linguistic expressions in describing video contents, and therefore, makes the
video captioning task even more challenging. In this paper, we propose an
unified caption framework, M&M TGM, which mines multimodal topics in
unsupervised fashion from data and guides the caption decoder with these
topics. Compared to pre-defined topics, the mined multimodal topics are more
semantically and visually coherent and can reflect the topic distribution of
videos better. We formulate the topic-aware caption generation as a multi-task
learning problem, in which we add a parallel task, topic prediction, in
addition to the caption task. For the topic prediction task, we use the mined
topics as the teacher to train a student topic prediction model, which learns
to predict the latent topics from multimodal contents of videos. The topic
prediction provides intermediate supervision to the learning process. As for
the caption task, we propose a novel topic-aware decoder to generate more
accurate and detailed video descriptions with the guidance from latent topics.
The entire learning procedure is end-to-end and it optimizes both tasks
simultaneously. The results from extensive experiments conducted on the MSR-VTT
and Youtube2Text datasets demonstrate the effectiveness of our proposed model.
M&M TGM not only outperforms prior state-of-the-art methods on multiple
evaluation metrics and on both benchmark datasets, but also achieves better
generalization ability.

该论文提出了一种基于 M&M TGM 的统一字幕生成框架，它利用从数据中无监督挖掘的多模态话题，并通过话题引导字幕解码器，通过并行任务主题预测来提高学习效果。该模型通过对 MSR-VTT 和 Youtube2Text 数据集进行广泛实验，证明了其在视频字幕生成方面的有效性，同时还具有更好的泛化能力。