Forecasting conversational derailment is the task of predicting, as the conversation unfolds, whether it will eventually derail into personal attacks. Since forecasting models operate in an online fashion, they must decide whether to "trigger" an alert after each utterance—for example, to notify participants or a moderator that the conversation is at risk of derailing. Existing approaches make this decision solely based on the estimated likelihood of derailment given the preceding utterances, implicitly assuming that the conversation’s future trajectory is fixed. As a result, they ignore the possibility of future recovery and incur an unnecessarily high rate of false positives.In this work we propose a method for decoupling the decision to trigger from derailment likelihood estimation. Our approach is inspired by the first human baseline on this task, which shows that humans achieve dramatically lower false positive rates by selectively deferring their decision to trigger when they anticipate that tension is likely to subside. We operationalize this insight with a deferral mechanism that uses forward-looking simulations to assess whether a tense moment admits plausible paths to recovery. Incorporating this mechanism into a state-of-the-art forecasting model substantially reduces false positives without sacrificing forecasting accuracy. More broadly, this work highlights the value of treating decision-making as a first-class component of forecasting systems.

Methods for lexical semantic-change detection quantify changes in the meaning of words over time. Prior methods have excelled on established benchmarks consisting of pre-selected target words, chosen ahead of time due to the prohibitive cost of manually annotating all words. However, performance measured on small curated wordsets cannot reveal how well these methods perform at discovering semantic changes among the full corpus vocabulary, which is the actual end goal for many applications. In this paper, we implement a top-k setup to evaluate semantic-change discovery despite lacking complete annotations. (At the same time, we also extend the annotations in the commonly used LiverpoolFC and SemEval-EN benchmarks by 85% and 90%, respectively). We deploy our evaluation setup on a battery of semantic-change detection methods under multiple variations. We find that when presented with a natural distribution of instances, all the methods struggle at ranking known large changes higher than other words in the vocabulary. Furthermore, we manually verify that the majority of words with high detected-change scores in LiverpoolFC do not actually experience meaning changes. In fact, for most of the methods, less than a half of the highest-ranked changes were determined to have changed in meaning. Given the large performance discrepancies between existing benchmark results and discovery “in the wild”, we recommend that researchers direct more attention to semantic-change discovery and include it in their suite of evaluations. Our annotations and code for running evaluations are available at https://github.com/khonzoda/semantic-change-discovery-emnlp2025.

We present a baseline for the CLPsych 2025 A.1 task: classifying self-states in mental health data taken from Reddit. We use few-shot learning with a 4-bit quantized Gemma 2 9B model (Gemma Team, 2024; Brown et al., 2020; Daniel Han and team, 2023) and a data preprocessing step which first identifies relevant sentences indicating self-state evidence, and then performs a binary classification to determine whether the sentence is evidence of an adaptive or maladaptive self-state. This system outperforms our other method which relies on an LLM to highlight spans of variable length independently. We attribute the performance of our model to the benefits of this sentence chunking step for two reasons: partitioning posts into sentences 1) broadly matches the granularity at which self-states were human-annotated and 2) simplifies the task for our language model to a binary classification problem. Our system placed third out of fourteen systems submitted for Task A.1, earning a test-time recall of 0.579.