Online learning platforms conduct exams to evaluate the learners in a
monotonous way, where the questions in the database may be classified into
Bloom's Taxonomy as varying levels in complexity from basic knowledge to
advanced evaluation. The questions asked in these exams to all learners are
very much static. It becomes important to ask new questions with different
difficulty levels to each learner to provide a personalized learning
experience. In this paper, we propose a multi-task method with an interactive
attention mechanism, Qdiff, for jointly predicting Bloom's Taxonomy and
difficulty levels of academic questions. We model the interaction between the
predicted bloom taxonomy representations and the input representations using an
attention mechanism to aid in difficulty prediction. The proposed learning
method would help learn representations that capture the relationship between
Bloom's taxonomy and difficulty labels. The proposed multi-task method learns a
good input representation by leveraging the relationship between the related
tasks and can be used in similar settings where the tasks are related. The
results demonstrate that the proposed method performs better than training only
on difficulty prediction. However, Bloom's labels may not always be given for
some datasets. Hence we soft label another dataset with a model fine-tuned to
predict Bloom's labels to demonstrate the applicability of our method to
datasets with only difficulty labels.

提出了 Qdiff 交互式注意力机制的多任务方法，用于预测学术问题的 Bloom's Taxonomy 和难度级别，并展示了该方法在难度标签数据集上的应用。

辅助任务引导的交互式关注模型用于问题难度预测

Auxiliary Task Guided Interactive Attention Model for Question Difficulty Prediction

In this paper, we focus on a new practical task, document-scale text content
manipulation, which is the opposite of text style transfer and aims to preserve
text styles while altering the content. In detail, the input is a set of
structured records and a reference text for describing another recordset. The
output is a summary that accurately describes the partial content in the source
recordset with the same writing style of the reference. The task is
unsupervised due to lack of parallel data, and is challenging to select
suitable records and style words from bi-aspect inputs respectively and
generate a high-fidelity long document. To tackle those problems, we first
build a dataset based on a basketball game report corpus as our testbed, and
present an unsupervised neural model with interactive attention mechanism,
which is used for learning the semantic relationship between records and
reference texts to achieve better content transfer and better style
preservation. In addition, we also explore the effectiveness of the
back-translation in our task for constructing some pseudo-training pairs.
Empirical results show superiority of our approaches over competitive methods,
and the models also yield a new state-of-the-art result on a sentence-level
dataset.

本文提出了一个新的实际任务，文档级文本内容操作，它旨在在更改内容的同时保留文本样式，并提出一种基于交互式注意机制的无监督神经模型，探索了在该任务中构建伪训练对的后向翻译方法的有效性。实证结果表明，该方法优于竞争方法，并且模型还在句子级数据集上产生了新的最优结果

学习选择两方面信息以进行文档级文本内容操作

Learning to Select Bi-Aspect Information for Document-Scale Text Content  Manipulation

Speech-to-text translation (ST), which translates source language speech into
target language text, has attracted intensive attention in recent years.
Compared to the traditional pipeline system, the end-to-end ST model has
potential benefits of lower latency, smaller model size, and less error
propagation. However, it is notoriously difficult to implement such a model
without transcriptions as intermediate. Existing works generally apply
multi-task learning to improve translation quality by jointly training
end-to-end ST along with automatic speech recognition (ASR). However, different
tasks in this method cannot utilize information from each other, which limits
the improvement. Other works propose a two-stage model where the second model
can use the hidden state from the first one, but its cascade manner greatly
affects the efficiency of training and inference process. In this paper, we
propose a novel interactive attention mechanism which enables ASR and ST to
perform synchronously and interactively in a single model. Specifically, the
generation of transcriptions and translations not only relies on its previous
outputs but also the outputs predicted in the other task. Experiments on TED
speech translation corpora have shown that our proposed model can outperform
strong baselines on the quality of speech translation and achieve better speech
recognition performances as well.

本文提出了一种新的交互式注意机制，使自动语音识别和语音翻译在单个模型中可以同步地和交互地进行，实验表明，该模型在语音翻译和语音识别方面的性能表现均优于基线模型。