Automated answer validation can help improve learning outcomes by providing
appropriate feedback to learners, and by making question answering systems and
online learning solutions more widely available. There have been some works in
science question answering which show that information retrieval methods
outperform neural methods, especially in the multiple choice version of this
problem. We implement Siamese neural network models and produce a generalised
solution to this problem. We compare our supervised model with other text
similarity based solutions.

自动答案验证可以通过为学习者提供适当的反馈并提供更广泛的问题回答系统和在线学习解决方案来改善学习成果。我们实现了一种共享神经网络模型，并提供了一个通用的解决方案，比较了我们的监督模型和其他基于文本相似度的解决方案。

文本相似性用于自动答案验证

Automated Answer Validation using Text Similarity

Resolving the contextual dependency is one of the most challenging tasks in
the Conversational system. Our submission to CAsT-2021 aimed to preserve the
key terms and the context in all subsequent turns and use classical Information
retrieval methods. It was aimed to pull as relevant documents as possible from
the corpus. We have participated in automatic track and submitted two runs in
the CAsT-2021. Our submission has produced a mean NDCG@3 performance better
than the median model.

在 CAsT-2021 的自动化轨道中，保留关键术语和上下文并使用经典的信息检索方法来解决会话系统中最具挑战性的任务之一 - 解决上下文依赖性。我们提交了两次运行，产生的平均 NDCG@3 性能比中位数模型更好。

IITD-DBAI: 带伪相关反馈和查询重组的多阶段检索

IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation

Retrieving paragraphs to populate a Wikipedia article is a challenging task.
The new TREC Complex Answer Retrieval (TREC CAR) track introduces a
comprehensive dataset that targets this retrieval scenario. We present early
results from a variety of approaches -- from standard information retrieval
methods (e.g., tf-idf) to complex systems that using query expansion using
knowledge bases and deep neural networks. The goal is to offer future
participants of this track an overview of some promising approaches to tackle
this problem.

介绍了用于填充维基百科文章的 TREC CAR 赛道，提供使用信息检索方法，查询扩展和深度神经网络等一系列系统解决这一难题的早期结果以帮助未来参与者。