Human speakers have an extensive toolkit of ways to express themselves.In this paper, we engage with an idea largely absent from discussions of meaning in natural language understanding-namely, that the way something is expressed reflects different ways of conceptualizing or construing the information being conveyed.We first define this phenomenon more precisely, drawing on considerable prior work in theoretical cognitive semantics and psycholinguistics.We then survey some dimensions of construed meaning and show how insights from construal could inform theoretical and practical work in NLP.

Paraphrasing is rooted in semantics. We show the effectiveness of transformers (Vaswani et al. 2017) for paraphrase generation and further improvements by incorporating PropBank labels via a multi-encoder. Evaluating on MSCOCO and WikiAnswers, we find that transformers are fast and effective, and that semantic augmentation for both transformers and LSTMs leads to sizable 2-3 point gains in BLEU, METEOR and TER. More importantly, we find surprisingly large gains on human evaluations compared to previous models. Nevertheless, manual inspection of generated paraphrases reveals ample room for improvement: even our best model produces human-acceptable paraphrases for only 28% of captions from the CHIA dataset (Sharma et al. 2018), and it fails spectacularly on sentences from Wikipedia. Overall, these results point to the potential for incorporating semantics in the task while highlighting the need for stronger evaluation.

We present a structured model of context that supports an integrated approach to language acquisition and use. The model extends an existing formal notation, Embodied Construction Grammar (ECG), with representations for tracking both entities and events in discourse and situational context. The notation employs an intermediate level of granularity between low-level sensorimotor representations (such as that suitable for dynamic models of action and events for grounded language learning) and the more schematic representations needed for learning and using grammar. The resulting model allows existing systems for simulation-based language understanding and comprehension-driven grammar learning to represent, interpret and acquire a variety of contextually grounded construction.

We present a stochastic finite-state model for segmenting Chinese text into dictionary entries and productively derived words, and providing pronunciations for these words; the method incorporates a class-based model in its treatment of personal names. We also evaluate the system's performance, taking into account the fact that people often do not agree on a single segmentation.