Existing question answering datasets focus on dealing with homogeneous information, based either only on text or KB/Table information alone. However, as human knowledge is distributed over heterogeneous forms, using homogeneous information alone might lead to severe coverage problems. To fill in the gap, we present HybridQA 1 , a new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. We test with three different models: 1) a table-only model.2) text-only model. 3) a hybrid model that combines heterogeneous information to find the answer. The experimental results show that the EM scores obtained by two baselines are below 20%, while the hybrid model can achieve an EM over 40%. This gap suggests the necessity to aggregate heterogeneous information in HybridQA. However, the hybrid model's score is still far behind human performance. Hence, HybridQA can serve as a challenging benchmark to study question answering with heterogeneous information.

Previous studies on Natural Language Generation (NLG) from structured data have primarily focused on surface-level descriptions of record sequences. However, for complex structured data, e.g., multi-row tables, it is often desirable for an NLG system to describe interesting facts from logical inferences across records. If only provided with the table, it is hard for existing models to produce controllable and high-fidelity logical generations. In this work, we formulate highfidelity NLG as generation from logical forms in order to obtain controllable and faithful generations. We present a new large-scale dataset, LOGIC2TEXT, with 10,753 descriptions involving common logic types paired with the underlying logical forms.The logical forms show diversified graph structure of free schema, which pose great challenges on the model's ability to understand the semantics. We experiment on (1) Fullysupervised training with the full datasets, and (2) Few-shot setting, provided with hundreds of paired examples; We compare several popular generation models and analyze their performances. We hope our dataset can encourage research towards building an advanced NLG system capable of natural, faithful, and human-like generation. The dataset and code is available at https://github. com/czyssrs/Logic2Text.

Semi-supervised learning (SSL) concerns how to improve performance via the usage of unlabeled data. Recent studies indicate that the usage of unlabeled data might even deteriorate performance. Although some proposals have been developed to alleviate such a fundamental challenge for semi-supervised classification, the efforts on semi-supervised regression (SSR) remain to be limited. In this work we consider the learning of a safe prediction from multiple semi-supervised regressors, which is not worse than a direct supervised learner with only labeled data. We cast it as a geometric projection issue with an efficient algorithm. Furthermore, we show that the proposal is provably safe and has already achieved the maximal performance gain, if the ground-truth label assignment is realized by a convex linear combination of base regressors. This provides insight to help understand safe SSR. Experimental results on a broad range of datasets validate the effectiveness of our proposal.

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