@inproceedings{bruendler-clematide-2026-maritimemails,
title = "{M}aritim{E}mails: A Synthetic Dataset for Maritime Chartering Correspondence",
author = "Bruendler, Kevin and
Clematide, Simon",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.599/",
doi = "10.63317/4ewa9vv654ty",
pages = "7556--7567",
abstract = "We introduce MaritimEmails, a large-scale synthetic corpus of 19,817 English-language email threads simulating maritime chartering negotiations between brokers and charterers. Email remains a dominant medium for business communication, yet no public corpora exist for this highly specialized domain due to confidentiality constraints. To address this gap, we generate domain-plausible negotiation exchanges using five contemporary language models under multiple prompting strategies, including Attribute Prompting and Base{--}Refine (BARE) approaches. Each thread includes structured annotations for vessels, ports, commodities, and Incoterms, enabling supervised training for information extraction and related tasks. Our comparative evaluation covering lexical and semantic diversity, sentiment balance, and verbosity shows that BARE generation increases linguistic variation while maintaining coherence. However, all models exhibit a systematic positivity bias, yielding less negative sentiment than is observed in the Enron reference corpus and likely also in many real negotiation settings. Baseline information extraction experiments with GLiNER and generative Qwen models yield up to 0.86 macro F1 on entity extraction, supporting the dataset{'}s usefulness. MaritimEmails, together with prompts, scripts, and documentation, is released for research use."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bruendler-clematide-2026-maritimemails">
<titleInfo>
<title>MaritimEmails: A Synthetic Dataset for Maritime Chartering Correspondence</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kevin</namePart>
<namePart type="family">Bruendler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Clematide</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stelios</namePart>
<namePart type="family">Piperidis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Núria</namePart>
<namePart type="family">Bel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Henk</namePart>
<namePart type="family">van den Heuvel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nancy</namePart>
<namePart type="family">Ide</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Krek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Toral</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resource Association</publisher>
<place>
<placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We introduce MaritimEmails, a large-scale synthetic corpus of 19,817 English-language email threads simulating maritime chartering negotiations between brokers and charterers. Email remains a dominant medium for business communication, yet no public corpora exist for this highly specialized domain due to confidentiality constraints. To address this gap, we generate domain-plausible negotiation exchanges using five contemporary language models under multiple prompting strategies, including Attribute Prompting and Base–Refine (BARE) approaches. Each thread includes structured annotations for vessels, ports, commodities, and Incoterms, enabling supervised training for information extraction and related tasks. Our comparative evaluation covering lexical and semantic diversity, sentiment balance, and verbosity shows that BARE generation increases linguistic variation while maintaining coherence. However, all models exhibit a systematic positivity bias, yielding less negative sentiment than is observed in the Enron reference corpus and likely also in many real negotiation settings. Baseline information extraction experiments with GLiNER and generative Qwen models yield up to 0.86 macro F1 on entity extraction, supporting the dataset’s usefulness. MaritimEmails, together with prompts, scripts, and documentation, is released for research use.</abstract>
<identifier type="citekey">bruendler-clematide-2026-maritimemails</identifier>
<identifier type="doi">10.63317/4ewa9vv654ty</identifier>
<location>
<url>https://aclanthology.org/2026.lrec-1.599/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>7556</start>
<end>7567</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MaritimEmails: A Synthetic Dataset for Maritime Chartering Correspondence
%A Bruendler, Kevin
%A Clematide, Simon
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F bruendler-clematide-2026-maritimemails
%X We introduce MaritimEmails, a large-scale synthetic corpus of 19,817 English-language email threads simulating maritime chartering negotiations between brokers and charterers. Email remains a dominant medium for business communication, yet no public corpora exist for this highly specialized domain due to confidentiality constraints. To address this gap, we generate domain-plausible negotiation exchanges using five contemporary language models under multiple prompting strategies, including Attribute Prompting and Base–Refine (BARE) approaches. Each thread includes structured annotations for vessels, ports, commodities, and Incoterms, enabling supervised training for information extraction and related tasks. Our comparative evaluation covering lexical and semantic diversity, sentiment balance, and verbosity shows that BARE generation increases linguistic variation while maintaining coherence. However, all models exhibit a systematic positivity bias, yielding less negative sentiment than is observed in the Enron reference corpus and likely also in many real negotiation settings. Baseline information extraction experiments with GLiNER and generative Qwen models yield up to 0.86 macro F1 on entity extraction, supporting the dataset’s usefulness. MaritimEmails, together with prompts, scripts, and documentation, is released for research use.
%R 10.63317/4ewa9vv654ty
%U https://aclanthology.org/2026.lrec-1.599/
%U https://doi.org/10.63317/4ewa9vv654ty
%P 7556-7567
Markdown (Informal)
[MaritimEmails: A Synthetic Dataset for Maritime Chartering Correspondence](https://aclanthology.org/2026.lrec-1.599/) (Bruendler & Clematide, LREC 2026)
ACL