Structural Causal Models (SCM) framework introduced by Pearl [2009].

Structural Causal Models (SCM) framework introduced by Pearl [2009].

Event argument extraction (EAE) identifies event arguments and their specific roles for a given event. Recent advancement in generation-based EAE models has shown great performance and generalizability over classification-based models. However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporating additional information that has been shown to be effective for classification-based models, such as the abstract meaning representation (AMR) of the input passages. Incorporating such information into generation-based models is challenging due to the heterogeneous nature of the natural language form prevalently used in generation-based models and the structured form of AMRs. In this work, we study strategies to incorporate AMR into generation-based EAE models. We propose AMPERE, which generates AMR-aware prefixes for every layer of the generation model. Thus, the prefix introduces AMR information to the generation-based EAE model and then improves the generation. We also introduce an adjusted copy mechanism to AMPERE to help overcome potential noises brought by the AMR graph. Comprehensive experiments and analyses on ACE2005 and ERE datasets show that AMPERE can get 4% - 10% absolute F1 score improvements with reduced training data and it is in general powerful across different training sizes.

Event extraction (EE) aims to identify structured events, including event triggers and their corresponding arguments, from unstructured text. Most of the existing works rely on a large number of labeled instances to train models, while the labeled data could be expensive to be obtained. In this work, we present a data-efficient event extraction method by formulating event extraction as a natural language generation problem. The formulation allows us to inject knowledge of label semantics, event structure, and output dependencies into the model. Given a passage and an event type, our model learns to summarize this passage into a templated sentence in a predefined structure. The template is event-type-specific, manually created, and contains event trigger and argument information. Lastly, a rule-based algorithm is used to derive the trigger and argument predictions from the generated sentence. Our method inherently enjoys the following benefits: (1) The pretraining of the generative language models help incorporate the semantics of the labels for generative EE. (2) The autoregressive generation process and our end-to-end design for extracting triggers and arguments force the model to capture the dependencies among the output triggers and their arguments. (3) The predefined templates form concrete yet flexible rules to hint the models about the valid patterns for each event type, reducing the models' burden to learn structures from the data. Empirical results show that our model achieves superior performance over strong baselines on EE tasks in the low data regime and achieves competitive results to the current state-of-the-art when more data becomes available.

We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs. We study this problem without the causal sufficiency assumption, using Maximal Ancestral Graphs (MAG) to model the causal graphs, and assuming that we have the ability to actively perform independent single vertex (or atomic) interventions on the entities. If the $M$ underlying (unknown) causal graphs of the entities satisfy a natural notion of clustering, we give algorithms that leverage this property and recovers all the causal graphs using roughly logarithmic in $M$ number of atomic interventions per entity. These are significantly fewer than $n$ atomic interventions per entity required to learn each causal graph separately, where $n$ is the number of observable nodes in the causal graph. We complement our results with a lower bound and discuss various extensions of our collaborative setting.