Jack Boylan, Chris Hokamp, Demian Gholipour Ghalandari. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the objective function(s) that are used for learning and inference.Recent unsupervised sentence compression approaches use custom objectives to guide discrete search; however, guided search is expensive at inference time.In this work, we explore the use of reinforcement learning to train effective sentence compression models that are also fast when generating predictions.In particular, we cast the task as binary sequence labelling and fine-tune a pre-trained transformer using a simple policy gradient approach.Our approach outperforms other unsupervised models while also being more efficient at inference time.

Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.However, there is a lack of datasets that realistically address such use cases at a scale large enough for training supervised models for this task.This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters.We build this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which provides concise and neutral human-written summaries of news events, with links to external source articles.We also automatically extend these source articles by looking for related articles in the Common Crawl archive.We provide a quantitative analysis of the dataset and empirical results for several state-of-the-art MDS techniques.The dataset is available at

We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the inclusion of pre-specified lexical constraints.The algorithm can be used with any model that generates a sequence ŷ = {y 0 . . .y T }, by maximizing p(y|x) = t p(y t |x; {y 0 . . .y t-1 }).Lexical constraints take the form of phrases or words that must be present in the output sequence.This is a very general way to incorporate additional knowledge into a model's output without requiring any modification of the model parameters or training data.We demonstrate the feasibility and flexibility of Lexically Constrained Decoding by conducting experiments on Neural Interactive-Predictive Translation, as well as Domain Adaptation for Neural Machine Translation.Experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).

We introduce an interactive interface that aims to help English as a Second Language (ESL) students overcome language related hindrances while reading a text.The interface allows the user to find supplementary information on selected difficult words.The interface is empowered by our lexical substitution engine that provides context-based synonyms for difficult words.We also provide a practical solution for a real-world usage scenario.We demonstrate using the lexical substitution engine -as a browser extension that can annotate and disambiguate difficult words on any webpage.