Although transfer learning has been shown to be successful for tasks like
object and speech recognition, its applicability to question answering (QA) has
yet to be well-studied. In this paper, we conduct extensive experiments to
investigate the transferability of knowledge learned from a source QA dataset
to a target dataset using two QA models. The performance of both models on a
TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson
et al., 2013) is significantly improved via a simple transfer learning
technique from MovieQA (Tapaswi et al., 2016). In particular, one of the models
achieves the state-of-the-art on all target datasets; for the TOEFL listening
comprehension test, it outperforms the previous best model by 7%. Finally, we
show that transfer learning is helpful even in unsupervised scenarios when
correct answers for target QA dataset examples are not available.

本文探究了迁移学习在问题回答方面的应用，使用两种问题回答模型，在 TOEFL 和 MCTest 数据集上通过简单的迁移学习技术从 MovieQA 数据集中学到的知识显著提高了性能，尤其是其中一种模型在所有目标数据集上取得了最佳效果，对于 TOEFL 听力理解测试，它的性能比以前的最佳模型提高了 7%。最后，我们证明了即使在目标 QA 数据集示例的正确答案不可用的无监督情况下，迁移学习也是有帮助的。

问答领域的有监督和无监督迁移学习

Supervised and Unsupervised Transfer Learning for Question Answering

Multimedia or spoken content presents more attractive information than plain
text content, but it's more difficult to display on a screen and be selected by
a user. As a result, accessing large collections of the former is much more
difficult and time-consuming than the latter for humans. It's highly attractive
to develop a machine which can automatically understand spoken content and
summarize the key information for humans to browse over. In this endeavor, we
propose a new task of machine comprehension of spoken content. We define the
initial goal as the listening comprehension test of TOEFL, a challenging
academic English examination for English learners whose native language is not
English. We further propose an Attention-based Multi-hop Recurrent Neural
Network (AMRNN) architecture for this task, achieving encouraging results in
the initial tests. Initial results also have shown that word-level attention is
probably more robust than sentence-level attention for this task with ASR
errors.

本研究提出了一种基于 AMRNN 的机器对英语口语内容进行听力理解测试，结果表明在这项任务中，单词级别的注意力机制比句子级别更为鲁棒。

面向机器理解口语内容：机器初步实现托福听力理解测试

Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening  Comprehension Test by Machine

This paper presents a simple unsupervised learning algorithm for recognizing
synonyms, based on statistical data acquired by querying a Web search engine.
The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and
Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR
is empirically evaluated using 80 synonym test questions from the Test of
English as a Foreign Language (TOEFL) and 50 synonym test questions from a
collection of tests for students of English as a Second Language (ESL). On both
tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent
Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL
questions. The paper discusses potential applications of the new unsupervised
learning algorithm and some implications of the results for LSA and LSI (Latent
Semantic Indexing).

本文提出了一种基于无监督学习的算法，使用从网络搜索引擎查询得到的统计数据来识别同义词，这种算法称为 PMI-IR。 PMI-IR 通过使用点对点互信息（PMI）和信息检索（IR）来测量单词对的相似度，并在 80 个 TOEFL 同义词测试问题和 50 个 ESL 英语学习者测试问题上进行了实证评估，得分分别为 74％和 64％，与 LSA 进行对比。本文讨论了新的无监督学习算法的潜在应用以及结果对 LSA 和 LSI 的影响。