Learning semantic correspondence between the structured data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Recent neural generation methods require to use large scale training data. However, the collected data-text pairs for training are usually loosely corresponded, where texts contain additional or contradicted information compare to its paired input. In this paper, we propose a local-to-global alignment (L2GA) framework to learn semantic correspondences from loosely related data-text pairs. First, a local alignment model based on multi-instance learning is applied to build the semantic correspondences within a data-text pair. Then, a global alignment model built on top of a memory guided conditional random field (CRF) layer is designed to exploit dependencies among alignments in the entire training corpus, where the memory is used to integrate the alignment clues provided by the local alignment model. Therefore, it is capable of inducing missing alignments for text spans that are not supported by its imperfect paired input. Experiments on recent restaurant dataset show that our proposed method can improve the alignment accuracy and as a by product, our method is also applicable to induce semantically equivalent training data-text pairs for neural generation models. Learning semantic correspondences between the structured data (e.g., slot-values pairs in a meaning representation (MR)) and associated description texts is one of core problem in NLP community (Barzilay & Lapata, 2005) , e.g., data-to-text generation produces texts based on the learned semantic correspondences. Recent data-to-text generation methods, especially neural-base methods which are data-hungry, adopt data-text pairs collected from web for training. Such collected corpus usually contain loosely corresponded data text pairs (Perez-Beltrachini & Gardent, 2017; Nie et al., 2019) , where text spans contain information that are not supported by its imperfect structured input. Figure 1 depicts an example, where the slot-value pair Price=Cheap can be aligned to text span low price range while the text span restaurant doesn't supported by any slot-value pair in paired input MR. Most of previous work for learning semantic correspondences (Barzilay & Lapata, 2005; Liang et al., 2009; Kim & Mooney, 2010; Perez-Beltrachini & Lapata, 2018) focus on characterizing local interactions between every text span with a corresponded slots presented in its paired MR. Such methods cannot work directly on loosely corresponded data-text pairs, as setting is different. In this work, we make a step towards explicit semantic correspondences (i.e., alignments) in loosely corresponded data text pairs. Compared with traditional setting, which only attempts inducing alignments for every text span with a corresponded slot presented in its paired MR. We propose a Local-to-Global Alignment (L2GA) framework, where the local alignment model discovers the correspondences within a single data-text pair (e.g., low price range is aligned with the slot Price in Figure 1 ) and a global alignment model exploits dependencies among alignments presented in the entire data-text pairs and therefore, is able to induce missing attributes for text spans not supported in its noisy input data (e.g., restaurant is aligned with the slot EatType in Figure 1 ). Specially, our proposed L2GA is composed of two parts. The local alignment model is a neural method optimized via a multi-instance learning paradigm (Perez-Beltrachini & Lapata, 2018) which automatically captures correspondences by maximizing the similarities between co-occurred slots and texts within a data-text pair. Our proposed global alignment model is a memory guided conditional random field (CRF) based sequence labeling framework. The CRF layer is able to learn dependencies among semantic labels over the entire corpus and therefore is suitable for inferring missing alignments of unsupported text spans. However, since there are no semantic labels provided for sequence labeling, we can only leverage limited supervision provided in a data-text pair. We start by generating pseudo labels using string matching heuristic between words and slots (e.g., Golden Palace is aligned with Name in Figure 1 ). The pseudo labels result in large portion of unmatched text spans (e.g., low price and restaurant cannot be directly matched in Figure 1 ), we tackle this challenge by: a) changing the calculation of prediction probability in CRF layer, where we sum probabilities over possible label sequences for unmatched text spans to allow inference on unmatched words; b) incorporating alignment results produced by the local alignment model as an additional memory to guide the CRF layer, therefore, the semantic correspondences captured by local alignment model can together work with the CRF layer to induce alignments locally and globally. We conduct experiments of our proposed method on a recent restaurant dataset, E2E challenge benchmark (Novikova et al., 2017a) , results show that our framework can improve the alignment accuracy with respect to previous methods. Moreover, our proposed method can explicitly detect unaligned errors presented in the original training corpus and provide semantically equivalent training data-text pairs for neural generation models. Experimental results also show that our proposed method can improve content consistency for neural generation models. In this paper, we study the problem of learning alignments in loosely related data-text pairs. We propose a local-to-global framework which not only induces semantic correspondences for words that are related to its paired input but also infers potential labels for text spans that are not supported by its incomplete input. We find that our proposed method improves the alignment accuracy, and can be of help to reduce the noise in original training corpus. In the future, we will explore more challenging datasets with more complex data schema. Under review as a conference paper at ICLR 2020 are 300 and 100 respectively. The dimensions of trainable hidden units in LSTMs are all set to 400. We first pre-train our local model for 5 epochs and then train our proposed local-to-global model jointly with 10 epochs according to validation set. During training, we regularize all layers with a dropout rate of 0.1. We use stochastic gradient descent (SGD) for optimisation with learning rate 0.015. The gradient is truncated by 5.
We propose a local-to-global alignment framework to learn semantic correspondences from noisy data-text pairs with weak supervision
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