We study semi-supervised sequence generation tasks, where the few labeled examples are too scarce to finetune a model, and meanwhile, few-shot prompted large language models (LLMs) exhibit room for improvement. In this paper, we present the discovery that a student model distilled from a few-shot prompted LLM can commonly generalize better than its teacher to unseen examples on such tasks. We find that the student is able to learn a general pattern from the high-quality pseudolabels produced by the teacher during knowledge distillation (KD), and favorably not a general pattern from the low-quality pseudolabels. Leveraging this discovery, we propose a new method, Multistage Collaborative Knowledge Distillation from an LLM (MCKD), for these tasks. MCKD first few-shot prompts an LLM to produce pseudolabels for unlabeled data. Then at each stage of an iterative KD process, a new pair of students is trained on disjoint partitions of the pseudolabeled data, and produces new and improved pseudolabels for their unseen partitions. We conduct extensive experiments on four syntactic and semantic parsing datasets and show the effectiveness of MCKD for low-resource semi-supervised sequence generation. On CRAFT biomedical parsing, for example, 3-stage MCKD with 50 labeled examples outperforms an LLM teacher and vanilla KD by 7.5% and 3.7% parsing F1, respectively, and matches the performance of supervised finetuning with 500 labeled examples.

We propose the use of modal dependency parses (MDPs) aligned with syntactic dependency parse trees as an avenue for the novel task of claim extraction. MDPs provide a document-level structure that links linguistic expression of events to the conceivers responsible for those expressions. By defining the event-conceiver links as claims and using subgraph pattern matching to exploit the complementarity of these modal links and syntactic claim patterns, we outline a method for aggregating and classifying claims, with the potential for supplying a novel perspective on large natural language data sets. Abstracting away from the task of claim extraction, we prototype an interpretable information extraction (IE) paradigm over sentence- and document-level parse structures, framing inference as subgraph matching and learning as subgraph mining. We make our code open-sourced at https://github.com/BBN-E/nlp-graph-pattern-matching-and-mining.

Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts. These events are reported at such a speed and scale as to be overwhelming. In this paper, we present ExcavatorCovid, a machine reading system that ingests open-source text documents (e.g., news and scientific publications), extracts COVID-19 related events and relations between them, and builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help government agencies alleviate the information overload, understand likely downstream effects of political and economic decisions and events related to the pandemic, and respond in a timely manner to mitigate the impact of COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic: analysts and decision makers will be empowered by Excavator to better understand and solve complex problems in the future. A demonstration video is available at https://vimeo.com/528619007.

We present the first Universal Dependencies treebank for Hittite. This paper expands on earlier efforts at Hittite corpus creation (Molina and Molin, 2016; Molina, 2016) and discussions of annotation guidelines for Hittite within the UD framework (Inglese, 2015; Inglese et al., 2018). We build on the expertise of the above works to create a small corpus which we hope will serve as a stepping-stone to more expansive UD treebanking for Hittite.