Syntactic and Semantic-driven Learning for Open Information Extraction (2020)

Abstract: One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora. The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem. In this paper, we propose a syntactic and semantic-driven learning approach, which can learn neural open IE models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervision. Specifically, we first employ syntactic patterns as data labelling functions and pretrain a base model using the generated labels. Then we propose a syntactic and semantic-driven reinforcement learning algorithm, which can effectively generalize the base model to open situations with high accuracy. Experimental results show that our approach significantly outperforms the supervised counterparts, and can even achieve competitive performance to supervised state-of-the-art (SoA) model.

## introduction open information extraction (open ie) aims to extract open-domain textual tuples consisting of a predicate and a set of arguments from massive and heterogeneous corpora @xcite . for example, a system will extract a tuple (parragon; operates; more than 35 markets) from the sentence "parragon operates more than 35 markets and has 10 offices.". in contrary to the traditional ie, open ie is completely domain-independent and does not require the predetermined relations. recently, open ie has gained much attention @xcite @xcite @xcite @xcite [parragon] arg1 [operates] p [more than 35 markets] arg2 and has 10 offices. parragon operates more than 35 markets and has 10 offices. figure 1 : the proposed open ie framework, which consists of two learning strategies: 1) syntactic patterns are used as data labelling functions and a base model is pretrained using the generated labels; 2) a syntactic and semantic-driven rl algorithm is used to generalize the base model to open situations. @xcite @xcite and most of current open ie systems employ end-to-end neural networks, which first encode a sentence using bi-lstms, then extract tuples by sequentially labelling all tokens in the sentence @xcite @xcite or generating the target tuples token-by-token @xcite @xcite . for example, to extract (parragon; operates; more than 35 markets), neural open ie systems will label the sentence as [b-arg 1 , b-p, b-arg 2 , i-arg 2 , i-arg 2 , i-arg 2 , o, o, o, o, o] or generate a token sequence [<arg 1 >, parragon, <p >, operates, <arg 2 >, more, than, 35, markets]. the neural open ie systems, unfortunately, rely on the large labelled corpus to achieve good performance, which is often expensive and labourintensive to obtain. furthermore, open ie needs to extract relations of unlimited types from open domain corpus, which further exacerbates the need for large labelled corpus. therefore, the labelled sentence: parragon operates more than 35 markets and has 10 offices. tuple: (parragon; operates; more than 35 markets) nsubj dobj high semantic smilarity figure 2 : an example of open ie extractions, where the extracted tuple follows the nsubj and dobj dependency structure and is highly semantic similar to the original sentence. to resolve the labelled data bottleneck, this paper proposes a syntactic and semantic-driven learning approach, which can learn neural open ie models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higherlevel supervisions. the motivation of our method is that, although tuple extraction is a hard task, its inverse problem -tuple assessment is easier to resolve by exploiting the syntactic regularities of relation expressions and the semantic consistency between a tuple and its original sentence. for example, figure 2 shows the arg1 "parragon" and the arg2 "more than 35 markets" follow the nsubj and dobj dependency structure, respectively. meanwhile, the extracted tuple (parragon; operates; more than 35 markets) has a high semantic similarity with its original sentence "parragon operates more than 35 markets and has 10 offices.". and we found that the syntactic regularities can be effectively captured using syntactic rules, and the semantic consistency can be effectively modelled using the recent powerful pre-trained models such as bert @xcite . based on the above observations, we propose two learning strategies to exploit syntactic and semantic knowledge for model learning. figure 1 illustrates the framework of our method. firstly, syntactic open ie patterns are used as data labelling functions, and a base model is pretrained using the noisy training corpus generated by these labelling functions. secondly, because the pattern-based labels are often noisy and with limited coverage, we further propose a reinforcement learning algorithm which uses syntactic and semantic-driven reward functions, which can effectively generalize the base model to open situations with high accuracy. these two strategies together will ensure the effective learning of open ie models: the data labelling function can pretrain a reasonable initial model so that the rl algorithm can optimize model more effec-tively; although the pattern-based labels are often noisy and with low coverage, the rl algorithm can generalize the model to open situations with high accuracy. we conducted experiments on three open ie benchmarks: oie2016 @xcite , web and nyt @xcite . experimental results show that the proposed framework significantly outperforms the supervised counterparts, and can even achieve competitive performance with the supervised soa approach. 1 the main contributions of this paper are: ## syntactic and semantic-driven learning for open ie in this section, we describe how to learn neural open ie models without any human-labelled data. two strategies are proposed to exploit syntactic and semantic knowledge as noisier, higher-level supervisions. firstly, the syntactic patterns are used as data labelling functions for heuristically labelling a training corpus. secondly, the syntactic and semantic coherence scores between the extracted tuples and their original sentences are used as reward functions for reinforcement learning. these two strategies together will ensure the effective learning of open ie systems: 1) although the labels generated by syntactic patterns are noisy and with limited coverage, they can pretrain a reasonable initial model; 2) starting from the pretrained model, the syntactic and semantic-based reward functions provide an effective way to generalize our model to open situations. in the following, we first introduce the neural networks used for open ie. then we describe how to pretrain a base open ie model using syntactic patterns as data labelling functions. finally, we generalize the base model using reinforcement learning with syntactic and semantic-driven rewards. ## conclusions this paper proposes an open ie learning approach, which can learn neural models without any humanlabelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervisions. specifically, two effective learning strategies are proposed, including the pattern-based data labelling functions and the syntactic and semanticdriven rl algorithm. experimental results show that our method significantly outperforms supervised counterparts, and can even achieve competitive performance to supervised soa model. furthermore, because labelled data is a common bottleneck in nlp, we believe our syntactic and semanticdriven learning approach can also be used for other nlp tasks, such as event extraction, etc.