Conditional Causal Relationships between Emotions and Causes in Texts (2020)

Abstract: The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.

## introduction recently, the research on the causal relationships between human emotions and their corresponding causes has received much attention. recognizing the causes of a specific emotion in a document is considered as more useful than only identifying the emotion, due to the great potential of helping people make reasonable decisions and avoid unnecessary loss @xcite @xcite . there are currently two well-designed tasks concerning the causal relationships between emotions and their causes, the emotion cause extraction (ece) task @xcite @xcite and the emotion-cause pair extraction (ecpe) task @xcite . specifically, the ece task focuses on extracting the causes for a given emotion, while the ecpe task focuses on extracting emotions and the corresponding causes as pairs. despite their increasing popularity, both tasks only aim to extract the clauses containing causal relationships, and have neglected the possibility that the context clauses may be indispensable for the extracted clauses to have a valid causal relationship. let us consider the following example: in the above example, the cause and emotion clauses may not have a causal relationship if we ignore the context clauses, since the reasons of not updating one's social media account can be more than the owner passing away (e.g., forgetting his/her password, using a new account, etc.). only when extra information contained in the context clauses is available, can these two clauses have a valid causal relationship. therefore, it is essential to consider the context clauses as seriously as the targeted emotion and cause clauses when determining their causal relationships. with these context-dependent causal relations figured out, more complete and meaningful information about these causal relationships can be extracted. specifically, we can learn that some types of events may evoke different types of emotions under different circumstances, while some will always evoke one type of emotion under any circumstance. such useful information about causal relationships can be beneficial in many emotion-related applications, such as accurately predicting one's emotions with context taken into consideration when a specific event occurs. despite the ubiquitousness of the contextdependent causal relationships described above, few works have paid attention to them. in this work, we articulate the importance of context in the problem of causal relationship recognition, and make the first step of studying such special causal relationships in text data. we propose a task to determine whether or not the input pair of emotion and cause clauses has a causal relationship given some specific context clauses. as our task is new without any existing dataset available, we manually label the documents in the ecpe dataset constructed by @xcite and follow the procedure of negative sampling @xcite to build our own dataset. the constructed dataset can be further processed and used in some other important tasks that we aim to focus on in the future, such as quantifying the effect of context on the causal relationships. furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results according to the characteristics of the input clauses. the experiments on our constructed dataset demonstrate the effectiveness and generality of our aggregation module. the contributions of this work can be summarized as follows. ## related works as known to all, context has been utilized in many text-related applications to provide semantic information and improve task performance @xcite @xcite . in traditional causal reasoning, the term "context" is mostly discussed in the task of causal effect estimation, which is to estimate the influence of the cause variable on the effect variable @xcite . in some cases, the change of the effect variable relies not only on the cause variable, but also on some other relevant variables which can be viewed as the context variables and called confounders. therefore, these confounders should be discovered and "removed" by some specially designed algorithms in order to accurately estimate the causal effect, such as propensity score method @xcite @xcite , front-door criterion @xcite , instrumental variable estimator combined with structural causal models or potential outcome framework @xcite , etc. unfortunately, most of these existing works are not designed for text data of unstructured data format. also, before the noisy causal effect from the confounders can be removed, these confounders need to be discovered and represented in an explicit form, which is another challenge for text data to fit in the existing causal effect estimation models. a potential solution proposed by @xcite is to represent the confounders in text data as the topic distribution vectors achieved by latent dirichlet allocation model. however, such a representation is not theoretically explainable to carry enough information to represent these confounders. also, before estimating the causal effect, we need to first discover these causal relationships which are determined together by cause and context in text data. the tasks concerning causality extraction in text can be mainly divided into two categories, causal phrase extraction and causal clause extraction, where the former focuses on extracting word ## task definition in this section, we first formally define the term "conditional" based on the concept of emotioncause pair used in the ece and ecpe tasks, and then formulate our proposed task based on such conditional emotion-cause pairs. as defined by @xcite , an emotioncause pair (ecp) contains an emotion clause indicating an emotion (e.g., happiness) and a set of corresponding cause clauses. we define "conditional ecp" as follows. definition 1 (conditional emotion-cause pair) if an emotion-cause pair is considered to have causal relationship only when a specific context is given, it is called a conditional emotion-cause pair. examples of documents with a conditional pair and a non-conditional pair can be found in definition 1 indicates that the conditional pairs should not be judged to have causal relationship when an irrelevant context or no context is given. considering such a property, our task is formulated as follows. the proposed task given a specific context con i and an emotion-cause pair x @xmath0, e i ) containing a set of cause clauses c i and an emotion clause e i , determine a binary label y i to indicate whether or not the input pair x i has causal relationship under the context con i . as defined above, our proposed task is not to directly distinguish the conditional pairs from the non-conditional ones. the reason is that the recognition of a conditional pair is based on its different labels under different contexts instead of the text itself. therefore, such a task formulation spares the models from worrying about how to transform the labels of causal relationship to the labels of conditional pairs, and simplifies the process of training. ## dataset construction different from the ecpe task @xcite , the ecps are directly given in our task and the goal of our task is to judge whether or not they are causally related under a specific context. therefore, our proposed task is new without any existing annotation available, so we construct our own dataset based on the ecpe dataset @xcite by the following two steps: manual annotation and negative sampling. ## architecture in this section, we introduce our architecture for our task, and propose a simple, general and effective prediction aggregation module based on the characteristics of conditional ecps. ## experiment in section 4, we have described the process of dataset construction and the details of the constructed dataset foot_1 . in this section, we conduct experimental studies to evaluate our approach, and analyze the experiment results pragmatically. ## conclusion and future work in this paper, we articulate the importance of context in determining the causal relationships between emotions and their causes. to address this problem, we define a new task of determining whether or not an input emotion-cause pair has a causal relationship under a specific context. we construct a dataset for our task through manual annotation and negative sampling based on the ecpe dataset. furthermore, we propose a prediction aggregation module (pam) with low computational complexity, to enable the models to dynamically adjust the final prediction according to the type of emotion-cause pair contained in a document. experiments demonstrate the effectiveness and generality of our proposed pam. in view of the importance of context in the conditional causal relationships we define in this work, what we have done is only the first step. there remain many important and interesting problems ahead of us. for example, how to quantify the effect of context on the targeted causal relationship is another important task to study this type of causal relationship. besides, how to enable the existing emotion-cause pair extraction models to consider the effect of context is also a meaningful task.