Question answering (QA) has recently shown impressive results for answering questions from customized domains.Yet, a common challenge is to adapt QA models to an unseen target domain.In this paper, we propose a novel selfsupervised framework called QADA for QA domain adaptation.QADA introduces a novel data augmentation pipeline used to augment training QA samples.Different from existing methods, we enrich the samples via hidden space augmentation.For questions, we introduce multi-hop synonyms and sample augmented token embeddings with Dirichlet distributions.For contexts, we develop an augmentation method which learns to drop context spans via a custom attentive sampling strategy.Additionally, contrastive learning is integrated in the proposed self-supervised adaptation framework QADA.Unlike existing approaches, we generate pseudo labels and propose to train the model via a novel attention-based contrastive adaptation method.The attention weights are used to build informative features for discrepancy estimation that helps the QA model separate answers and generalize across source and target domains.To the best of our knowledge, our work is the first to leverage hidden space augmentation and attention-based contrastive adaptation for self-supervised domain adaptation in QA.Our evaluation shows that QADA achieves considerable improvements on multiple target datasets over state-of-the-art baselines in QA domain adaptation.

Question generation has recently shown impressive results in customizing question answering (QA) systems to new domains.These approaches circumvent the need for manually annotated training data from the new domain and, instead, generate synthetic questionanswer pairs that are used for training.However, existing methods for question generation rely on large amounts of synthetically generated datasets and costly computational resources, which render these techniques widely inaccessible when the text corpora is of limited size.This is problematic as many niche domains rely on small text corpora, which naturally restricts the amount of synthetic data that can be generated.In this paper, we propose a novel framework for domain adaptation called contrastive domain adaptation for QA (CAQA).Specifically, CAQA combines techniques from question generation and domaininvariant learning to answer out-of-domain questions in settings with limited text corpora.Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective.By combining techniques from question generation and domain-invariant learning, our model achieved considerable improvements compared to stateof-the-art baselines.

Learning to predict solutions to real-valued combinatorial graph problems promises efficient approximations. As demonstrated based on the NP-hard edge clique cover number, recurrent neural networks (RNNs) are particularly suited for this task and can even outperform state-of-the-art heuristics. However, the theoretical framework for estimating real-valued RNNs is understood only poorly. As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs. While such derivations have been made earlier for feed-forward and convolutional neural networks, our work presents the first such attempt for recurrent neural networks. Given a single-layer RNN with a rectified linear units and input of length b, we show that a population prediction error of ε can be realized with at most Õ(a4b/ε2) samples.1 We further derive comparable results for multi-layer RNNs. Accordingly, a size-adaptive RNN fed with graphs of at most n vertices can be learned in Õ(n6/ε2), i.,e., with only a polynomial number of samples. For combinatorial graph problems, this provides a theoretical foundation that renders RNNs competitive.

Knowledge bases (KBs) are essential for many downstream NLP tasks, yet their prime shortcoming is that they are often incomplete.State-of-the-art frameworks for KB completion often lack sufficient accuracy to work fully automated without human supervision.As a remedy, we propose IntKB: a novel interactive framework for KB completion from text based on a question answering pipeline.Our framework is tailored to the specific needs of a human-in-the-loop paradigm: (i) We generate facts that are aligned with text snippets and are thus immediately verifiable by humans.(ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero-and few-shot relations over time.(iii) We only trigger human interactions when there is enough information for a correct prediction.Therefore, we train our system with negative examples and a fold-option if there is no answer.Our framework yields a favorable performance: it achieves a hit@1 ratio of 29.7% for initially unseen relations, upon which it gradually improves to 46.2%.

State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive.For this reason, customizing QA systems is challenging.As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme.The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations.Human annotators then simply provide binary feedback on these candidates.Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost.To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost.We compare our framework against traditional manual annotations in an extensive set of experiments.We find that our approach can reduce up to 21.1% of the annotation cost.

The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer.However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused.In contrast, this work proposes RankQA 1 : RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer reranking.The re-ranking leverages different features that are directly extracted from the QA pipeline, i. e., a combination of retrieval and comprehension features.While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves stateof-the-art performance on 3 out of 4 benchmark datasets.Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic.Here the answer reranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size.As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.

Question answering promises a means of efficiently searching web-based content repositories such as Wikipedia. However, the systems of this type most prevalent today merely conduct their learning once in an offline training phase while, afterwards, all parameters remain static. Thus, the possibility of improvement over time is precluded. As a consequence of this shortcoming, question answering is not currently taking advantage of the wealth of feedback mechanisms that have become prominent on the web (e. g., buttons for liking, voting, or sharing).

State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer.Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved.We show that choosing a static number of documents -as used in prior research -suffers from a noiseinformation trade-off and yields suboptimal results.As a remedy, we propose an adaptive document retrieval model.This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query.We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.