Clinical trials are critical for advancing medical treatments but remain prohibitively expensive and time-consuming.Accurate prediction of clinical trial outcomes can significantly reduce research and development costs and accelerate drug discovery.While recent deep learning models have shown promise by leveraging unstructured data, their black-box nature, lack of interpretability, and vulnerability to label leakage limit their practical use in high-stakes biomedical contexts.In this work, we propose AUTOCT 1 , a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning.AUTOCT autonomously generates, evaluates, and refines tabular features based on public information without human input.Our method uses Monte Carlo Tree Search to iteratively optimize predictive performance.Experimental results show that AUTOCT performs on par with or better than state-of-the-art methods on clinical trial prediction tasks within only a limited number of self-refinement iterations, establishing a new paradigm for scalable, interpretable, and cost-efficient clinical trial prediction.

Event causality identification (ECI), a process that extracts causal relations between events from text, is crucial for distinguishing causation from correlation.Traditional approaches to ECI have primarily utilized linguistic patterns and multi-hop relational inference, risking false causality identification due to informal usage of causality and specious graphical inference.In this paper, we adopt the Rubin Causal Model to identify event causality: given two temporally ordered events, we see the first event as the treatment and the second one as the observed outcome.Determining their causality involves manipulating the treatment and estimating the resultant change in the likelihood of the outcome.Given that it is only possible to implement manipulation conceptually in the text domain, as a work-around, we try to find a 'twin' for the protagonist from existing corpora.This 'twin' should have identical life experiences with the protagonist before the treatment but undergoes an intervention of treatment.However, the practical difficulty of locating such a match limits its feasibility.Addressing this issue, we use the synthetic control method to generate such a 'twin' from relevant historical data, leveraging text embedding synthesis and inversion techniques.This approach allows us to identify causal relations more robustly than previous methods, including GPT-4, which is demonstrated on a causality benchmark, COPES-hard.

Temporal reasoning is the task of predicting temporal relations of event pairs.While temporal reasoning models can perform reasonably well on in-domain benchmarks, we have little idea of these systems' generalizability due to existing datasets' limitations.In this work, we introduce a novel task named TODAY that bridges this gap with temporal differential analysis, which as the name suggests, evaluates whether systems can correctly understand the effect of incremental changes.Specifically, TODAY introduces slight contextual changes for given event pairs, and systems are asked to tell how this subtle contextual change would affect relevant temporal relation distributions.To facilitate learning, TODAY also annotates human explanations.We show that existing models, including GPT-3.5, drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions.On the other hand, we show that TODAY's supervision style and explanation annotations can be used in joint learning, encouraging models to use more appropriate signals during training and thus outperform across several benchmarks.TODAY can also be used to train models to solicit incidental supervision from noisy sources such as GPT-3.5, thus moving us more toward the goal of generic temporal reasoning systems.

In this paper, we seek to improve the faithfulness of TEMPREL extraction models from two perspectives.The first perspective is to extract genuinely based on contextual description.To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label bias.We also add tense information into event representations to explicitly place an emphasis on the contextual description.The second perspective is to provide proper uncertainty estimation and abstain from extraction when no relation is described in the text.By parameterization of Dirichlet Prior over the model-predicted categorical distribution, we improve the model estimates of the correctness likelihood and make TEMPREL predictions more selective.We also employ temperature scaling to recalibrate the model confidence measure after bias mitigation.Through experimental analysis on MATRES, MATRES-DS, and TDDiscourse, we demonstrate that our model extracts TEMPREL and timelines more faithfully compared to SOTA methods, especially under distribution shifts.

Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps).A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal.In this paper, we address this gap by examining the extent to which current models comprehend the essentiality of step events in relation to a goal event.Cognitive studies suggest that such capability enables machines to emulate human commonsense reasoning about preconditions and necessary efforts of everyday tasks.We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow, with steps manually annotated for their essentiality concerning the goal by experts.The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality.However, after evaluating multiple statistical and largescale pre-trained language models, we find that existing approaches considerably underperform compared to humans.This observation highlights the need for further exploration into this critical and challenging task 1 .

Event mentions in text correspond to realworld events of varying degrees of granularity.The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes.Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EVENTSEG) as an auxiliary task to improve the learning for subevent detection.To bridge the two tasks together, we propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EVENTSEG prediction, as well as guiding the model to make globally consistent inference.Specifically, we adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.Experimental results show that the proposed method outperforms baseline methods by 2.3% and 2.5% on benchmark datasets for subevent detection, HiEve and IC, respectively, while achieving a decent performance on EVENTSEG prediction 1 .

This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing, that, given an event process, attempts to infer free-form type labels describing (i) the type of action made by the process and (ii) the type of object the process seeks to affect.This task is inspired by computational and cognitive studies of event understanding, which suggest that understanding processes of events is often directed by recognizing the goals, plans or intentions of the protagonist(s).We develop a large dataset containing over 60k event processes, featuring ultra fine-grained typing on both the action and object type axes with very large (10 3 ∼ 10 4 ) label vocabularies.We then propose a hybrid learning framework, P2GT, which addresses the challenging typing problem with indirect supervision from glosses 1 and a joint learning-to-rank framework.As our experiments indicate, P2GT supports identifying the intent of processes, as well as the fine semantic type of the affected object.It also demonstrates the capability of handling fewshot cases, and strong generalizability on outof-domain processes.2 * This work was done when the author was visiting the University of Pennsylvania.1 A gloss provides a sense definition for a lexeme. 2 The contributed learning resources, software and a system demonstration are available at http://cogcomp.org/page/publication_view/915.

Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other.In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them.Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations.Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations by converting these constraints into differentiable learning objectives.We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process.We also present a promising case study showing the effectiveness of our approach in inducing event complexes on an external corpus. 1

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories.Thus, knowledge about a known process such as "buying a car" can be used in the context of a new but analogous process such as "buying a house".Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction.In this paper, we propose an Analogous Process Structure Induction (APSI) framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes.As our experiments and analysis indicate, APSI 1 supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.