Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
- URL: http://arxiv.org/abs/2509.10644v2
- Date: Wed, 24 Sep 2025 16:59:55 GMT
- Title: Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
- Abstract summary: This paper situates the disconnect between computational morphology and language documentation within a broader misalignment between research and practice in NLP.<n>We argue that the field risks becoming decontextualized and ineffectual without systematic integration of User-Centered Design (UCD)
- Score: 20.169593321938695
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computational morphology has the potential to support language documentation through tasks like morphological segmentation and the generation of Interlinear Glossed Text (IGT). However, our research outputs have seen limited use in real-world language documentation settings. This position paper situates the disconnect between computational morphology and language documentation within a broader misalignment between research and practice in NLP and argues that the field risks becoming decontextualized and ineffectual without systematic integration of User-Centered Design (UCD). To demonstrate how principles from UCD can reshape the research agenda, we present a case study of GlossLM, a state-of-the-art multilingual IGT generation model. Through a small-scale user study with three documentary linguists, we find that despite strong metric based performance, the system fails to meet core usability needs in real documentation contexts. These insights raise new research questions around model constraints, label standardization, segmentation, and personalization. We argue that centering users not only produces more effective tools, but surfaces richer, more relevant research directions
Related papers
- Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding [88.11781604392606]
This paper defines language representation as the linguistic and symbolic constructs used to map and model the real world.<n>We argue that shaping schemas through advanced language representation is the next frontier for expanding Large Language Models.
arXiv Detail & Related papers (2026-05-10T02:42:29Z) - dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model [24.35392364602848]
dots.ocr is a Vision-Language Model that learns three core tasks within a unified, end-to-end framework.<n>This is made possible by a highly scalable data engine that synthesizes a vast multilingual corpus.<n>The efficacy of our unified paradigm is validated by state-of-the-art performance on the comprehensive OmniDocBench.
arXiv Detail & Related papers (2025-12-02T07:42:38Z) - Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning [12.377363857246602]
We propose a novel model named MI-DELIGHT for short text classification.<n>It first performs multi-source information exploration to alleviate the sparsity issues.<n>Then, the graph learning approach is adopted to learn the representation of short texts.
arXiv Detail & Related papers (2025-01-16T00:26:15Z) - Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature [5.773921786449337]
Section identification is an important task for library science, especially knowledge management.<n>We study the paper section identification problem in the context of Chinese medical literature analysis.
arXiv Detail & Related papers (2024-12-15T09:11:14Z) - Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing.
We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM.
We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
arXiv Detail & Related papers (2024-10-01T04:20:14Z) - Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML)
This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature.
The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
arXiv Detail & Related papers (2024-07-17T20:01:21Z) - Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks.
We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement.
We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
arXiv Detail & Related papers (2024-07-04T16:41:08Z) - Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models.
We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
arXiv Detail & Related papers (2024-04-09T11:39:53Z) - Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language
Modelling [70.23876429382969]
We propose a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks.
Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena.
For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge.
arXiv Detail & Related papers (2023-07-16T15:18:25Z) - Topic Discovery via Latent Space Clustering of Pretrained Language Model
Representations [35.74225306947918]
We propose a joint latent space learning and clustering framework built upon PLM embeddings.
Our model effectively leverages the strong representation power and superb linguistic features brought by PLMs for topic discovery.
arXiv Detail & Related papers (2022-02-09T17:26:08Z) - Incorporating Linguistic Knowledge for Abstractive Multi-document
Summarization [20.572283625521784]
We develop a neural network based abstractive multi-document summarization (MDS) model.
We process the dependency information into the linguistic-guided attention mechanism.
With the help of linguistic signals, sentence-level relations can be correctly captured.
arXiv Detail & Related papers (2021-09-23T08:13:35Z) - Positioning yourself in the maze of Neural Text Generation: A
Task-Agnostic Survey [54.34370423151014]
This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc.
We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
arXiv Detail & Related papers (2020-10-14T17:54:42Z) - Intrinsic Probing through Dimension Selection [69.52439198455438]
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks.
Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it.
In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted.
arXiv Detail & Related papers (2020-10-06T15:21:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.