# CoAM: Corpus of All-Type Multiword Expressions

arXiv: https://arxiv.org/abs/2412.18151 · HTML: https://mlsanity.com/paper/2412.18151/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding](https://mlsanity.com/paper/2204.10050/) (2022)
- [Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts](https://mlsanity.com/paper/2409.06790/) (2024)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [Lexical Semantic Recognition](https://mlsanity.com/paper/2004.15008/) (2020)
- [MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models](https://mlsanity.com/paper/2011.02541/) (2020)
- [Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions](https://mlsanity.com/paper/1902.10667/) (2019)
- [Detecting Multiword Expression Type Helps Lexical Complexity Assessment](https://mlsanity.com/paper/2005.05692/) (2020)

## Built on by (most cited first, 4 of 4)

- [LLMs as a synthesis between symbolic and distributed approaches to language](https://mlsanity.com/paper/2502.11856/) (2025)
- [Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement](https://mlsanity.com/paper/2601.19360/) (2026)
- [SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://mlsanity.com/paper/2602.09866/) (2026)
- [Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025](https://mlsanity.com/paper/2606.02255/) (2026)

## Abstract (arXiv)

Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation, but existing datasets for the task are inconsistently annotated, limited to a single type of MWE, or limited in size. To enable reliable and comprehensive evaluation, we created CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences constructed through a multi-step process to enhance data quality consisting of human annotation, human review, and automated consistency checking. Additionally, for the first time in a dataset of MWE identification, CoAM's MWEs are tagged with MWE types, such as Noun and Verb, enabling fine-grained error analysis. Annotations for CoAM were collected using a new interface created with our interface generator, which allows easy and flexible annotation of MWEs in any form. Through experiments using CoAM, we find that a fine-tuned large language model outperforms MWEasWSD, which achieved the state-of-the-art performance on the DiMSUM dataset. Furthermore, analysis using our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to identify across approaches.