Effect Generation Based on Causal Reasoning (2021)

Abstract: Causal reasoning aims to predict the future scenarios that may be caused by the observed actions. However, existing causal reasoning methods deal with causalities on the word level. In this paper, we propose a novel event-level causal reasoning method and demonstrate its use in the task of effect generation. In particular, we structuralize the observed cause-effect event pairs into an event causality network, which describes causality dependencies. Given an input cause sentence, a causal subgraph is retrieved from the event causality network and is encoded with the graph attention mechanism, in order to support better reasoning of the potential effects. The most probable effect event is then selected from the causal subgraph and is used as guidance to generate an effect sentence. Experiments show that our method generates more reasonable effect sentences than various well-designed competitors.

## introduction causal reasoning is the process of observing an action and reasoning future scenarios that may be potentially caused by it @xcite . earlier causal reasoning methods @xcite collect causally related word pairs (e.g., @xmath0, and then predict effects words for given cause words. recently, @xcite uses causal embedding to predict possible effect words of the input causes. @xcite proposed the lexically-constrained beam-search to generate possible effects given provided word guidance. however, all these methods tend to reason causalities at word-level. causalities between word pairs are not always self-contained (i.e., intelligible) when they are extracted without the context @xcite . for example, "@xmath1: "they always @xmath2. a word-level causal reasoning method may only predict the unintelligible effect of "break" conditioned "quarrel". considering this deficiency, a better way is to enhance causal reasoning with causal events @xcite @xcite . however, an observed causal event is very likely to appear only once, which brings about huge sparsity to causalities and great difficulty to the eventlevel causal reasoning. to solve this problem, we structuralize observed causal events into an event causality network, where similar events are clustered together. given an input cause sentence, a causal subgraph is retrieved and is encoded with the graph attention mechanism, in order to support better effect reasoning. as such, we are able to predict the most reasonable effect event based on the event causality network. the predicted effect event contains the skeleton information, with the detailed context neglected in the event extraction process. so we further rewrite the predicted effect event to an effect sentence in order to fill in the missing information. the contributions of this paper are twofold: i) we devise a effect generation method which is based on causal event reasoning (egcer) to generate effect sentences for given input cause sentences, ii) experiments demonstrate that our model achieves better performances compared among various welldesigned baselines. ## event causality network construction in this paper, we use causal events to bridge the causalities between input sequences and generated sequences. hence, we must first collect sufficient cause-effect sentence pairs so that from each sentence pair a cause-effect event pair can be eventified. then we construct an event causality network based on the extracted causal event pairs. the construction process includes two steps: 1) event eventification, 2) events structuralization. event eventification: following @xcite @xcite , we make use of a few highprecision causal connectives to extract cause-effect sentence pairs, for example 'because', 'as a result', etc. then we extract causal event pairs from causal sentence pairs based on dependency analysis 1 . we adopt the commonly used 4-tuple event representation (s, v, o, m) @xcite where v denotes the verb, s denotes the head noun of the subject, o denotes the head noun of the direct object or the adjective, and m denotes the head noun of the prepositional or indirect object. events structuralization: we structuralize the extracted causal event pairs into an event causality network, in which semantically similar events are clustered together. we use event abstractions to judge whether two events are semantically similar. the abstraction of an event is obtained by generalizing its components to their categories in linguistic resources. specifically, the verb in each event is generalized to its class in verbnet @xcite . the other components are generalized by the wordnet @xcite synset two levels up in the inherited hypernym hierarchy. in addition, we explicitly use the semantic-similarity based inferring rule. for example, assume we have observed that a has the same abstraction with b, and a causal relation holds from a to c, then it is most likely to conclude that there may be a causal relation from b to c. such a manipulation significantly reduces the sparsity of causalities in the event causality network, and hence supports better reasoning about the effect events. the weight of an edge in our event causality network is derived by the following rules: ## effect generation task discription: the goal of effect generation consists of predicting the an effect event for the input cause and rewriting the predicted effect event into an effect sentence. formally, given a cause sentence @xmath3, and a causal subgraph @xmath4, e 2 , • • • , e n cg }, which consists of a set of events {e @xmath5, v j , o j , m j )} (@xmath6, • • • , n cg ) as nodes, our model first predicts an effect event e y from cg according to x, then rewrites e y to an effect sentence @xmath7. the overview of the proposed egcer is illustrated in figure 1, which consists of two modules: 1) effect event predictor, and 2) effect event rewriter. effect event predictor: given the cause sentence x, a bidirectional gru model @xcite is used to reads the sequence x from both directions and computes hidden states -→ h x i and ←h x i for the token x i . the final hidden vectors of x is we then eventify the cause event from x, and match the event abstraction in the event causality network. once the abstraction is matched, a l-hop causal-related subgraph cg is preserved. the neighborhood information in cg represents the causality tendencies, which are useful for reasoning the most reasonable effect event. we use a simple graph neural network (gnn) @xcite to capture the neighborhood information. specifically, the lth layer's vectors of e i and its neighbors are pooled to obtain the vector of e i on the (l + 1)-th layer with a activation function σ (relu by default): ## conclusion and future work we present an event-level causal reasoning based effect generation method to generate the plausible effect sentences for the input cause sentences. experiments show that our method performs better than competitors in capturing the causal semantics which should be generated. in the future, we would like to develop more effective approaches to enhance the effect event reasoning, and more powerful generators to generate the effect sentences with higher quality.