Current textual question answering models achieve strong performance on
in-domain test sets, but often do so by fitting surface-level patterns in the
data, so they fail to generalize to out-of-distribution settings. To make a
more robust and understandable QA system, we model question answering as an
alignment problem. We decompose both the question and context into smaller
units based on off-the-shelf semantic representations (here, semantic roles),
and align the question to a subgraph of the context in order to find the
answer. We formulate our model as a structured SVM, with alignment scores
computed via BERT, and we can train end-to-end despite using beam search for
approximate inference. Our explicit use of alignments allows us to explore a
set of constraints with which we can prohibit certain types of bad model
behavior arising in cross-domain settings. Furthermore, by investigating
differences in scores across different potential answers, we can seek to
understand what particular aspects of the input lead the model to choose the
answer without relying on post-hoc explanation techniques. We train our model
on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets.
The results show that our model is more robust cross-domain than the standard
BERT QA model, and constraints derived from alignment scores allow us to
effectively trade off coverage and accuracy.

提出一种将问题回答建模为一种对齐问题的结构支持向量机方法，通过将问题和上下文分解成基于语义角色的单元，并将问题与上下文的子图进行对齐以找到答案，该模型可用于跨领域问题回答，且通过对齐得分派生出的限制使模型更加鲁棒。

通过子部分对齐实现鲁棒的问答

Robust Question Answering Through Sub-part Alignment

IllinoisSL is a Java library for learning structured prediction models. It
supports structured Support Vector Machines and structured Perceptron. The
library consists of a core learning module and several applications, which can
be executed from command-lines. Documentation is provided to guide users. In
Comparison to other structured learning libraries, IllinoisSL is efficient,
general, and easy to use.

IllinoisSL 是一个机器学习的库，用于学习结构化预测模型，包括支持结构化支持向量机和结构化感知机，相比其他结构化学习库，IllinoisSL 高效、通用且易于使用。