The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling

Incremental dialogue model components produce a sequence of output prefixes based on incoming input. Mistakes can occur due to local ambiguities or to wrong hypotheses, making the ability to revise past outputs a desirable property that can be governed by a policy. In this work, we formalise and characterise edits and revisions in incremental sequence labelling and propose metrics to evaluate revision policies. We then apply our methodology to profile the incremental behaviour of three Transformer-based encoders in various tasks, paving the road for better revision policies.Comment: Accepted at SIGdial 202

A Systematic Approach to Constructing Incremental Topology Control Algorithms Using Graph Transformation

Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approachComment: This document corresponds to the accepted manuscript of the referenced journal articl

Decision Strategies for Incremental POS Tagging

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 26-33. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Grammatical Encoding for Speech Production

This Elements series presents theoretical and empirical studies in the interdisciplinary field of psycholinguistics. Topics include issues in the mental representation and processing of language in production and comprehension, and the relationship of psycholinguistics to other fields of research. Each Element is a high quality and up-to-date scholarly work in a compact, accessible format.Publisher PD

Probing Brain Context-Sensitivity with Masked-Attention Generation

Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration. To address these questions we introduce a new approach named masked-attention generation. It uses GPT-2 transformers to generate word embeddings that capture a fixed amount of contextual information. We then tested whether these embeddings could predict fMRI brain activity in humans listening to naturalistic text. The results showed that most of the cortex within the language network is sensitive to contextual information, and that the right hemisphere is more sensitive to longer contexts than the left. Masked-attention generation supports previous analyses of context-sensitivity in the brain, and complements them by quantifying the window size of context integration per voxel.Comment: 2 pages, 2 figures, CCN 202

Gaining Access to the Language of Science: A Research Partnership for Disciplined, Discursive Ways to Select and Assess Vocabulary Knowledge

To equalize access to science learning across genders and demographic groups, access to the disciplinary language of science is one place to start. The language of science is highly challenging and specialized, and difficulties acquiring this language contribute to disparities in science achievement across diverse student groups. This study used a pre-post design to analyze effectiveness of a brief classroom science vocabulary assessment designed to assess receptive and productive vocabulary knowledge across multiple sections of one 7th grade science teacher’s class. Vocabulary was selected and analysis conducted by an interdisciplinary research partnership including the science teacher, a literacy specialist, and a scientist. The resulting model presents an assessment that evaluates receptive knowledge and productive use of science language and reinforces vocabulary theory: learning words is incremental and multidimensional, and assessment should address this specialized skill in principled, disciplined ways