---
title: "“I Object!” Modeling Latent Pragmatic Effects in Courtroom"
description: "The paper asks whether a system can learn which turns in a courtroom exchange provoke an objection… 26 references, cited by 1 paper here."
authors: ["Dan Goldwasser", "Hal Daumé III"]
year: 2014
venue: "Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics"
source: "https://aclanthology.org/E14-1069/"
doi: "10.3115/v1/e14-1069"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goldwasser-2014-i-object-modeling-latent-pragmatic/"
---
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# “I Object!” Modeling Latent Pragmatic Effects in Courtroom Dialogues

**Authors:** [Dan Goldwasser](https://md.research.elamur.ai/free/en/authors/dan-goldwasser/) and [Hal Daumé III](https://md.research.elamur.ai/free/en/authors/hal-daume-iii/) · **Year:** 2014 · **Venue:** [Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2014](https://md.research.elamur.ai/free/en/venues/eacl/2014/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/E14-1069/) · [PDF](https://aclanthology.org/E14-1069.pdf) · [DOI 10.3115/v1/e14-1069](https://doi.org/10.3115/v1/e14-1069) · [OpenAlex](https://openalex.org/W2250353434)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks whether a system can learn which turns in a courtroom exchange provoke an objection, and why. Labeling the pragmatic rule behind each objection by hand would be costly and subjective. The authors instead take supervision directly from the transcripts. They search the O.J. Simpson murder trial record for judge rulings ("sustain"/"overrule"), which give a binary label, an objection type and the ruling. The few turns before each ruling form the input snippet.

A discriminative latent-variable model, with inference cast as an ILP, makes two kinds of hidden decisions:
- which individual sentences matter for the decision;
- which sentence pairs across turns are linked, such as a question and its answer.

These decisions are learned jointly with the objection classifier. Lexical features are enriched with lexicons for subjectivity, hedging and bias, plus named entities. Every feature is conditioned on the speaker's identity, role and side in the trial.

The learned dialogue structure clearly improves classification, and speaker information makes that structure more sensible. The broader point is that an outcome speech act can serve as cheap distant supervision for discourse structure.

## Key points
- **Data and context window:** objections are mined automatically from the O.J. Simpson trial transcripts by matching sustain/overrule lemmas in the judge's turns. The previous n=6 turns are treated as possible causes. Dialogues average 8.6 sentences.
- **Linguistic resources:**
  - Stanford NER (Finkel et al., 2005)
  - the subjectivity/sentiment lexicon of Riloff and Wiebe (2003)
  - Hyland's (2005) hedges and boosters
  - the Wikipedia-derived bias lexicon of Recasens et al. (2013)
  - Patient Polarity Verbs (Goyal et al., 2010)
- **Sentence labels:** each turn is split into sentences tagged FORMALITY, QUESTION, RESPONSE (POSITIVE/NEGATIVE) or STATEMENT.
- **Model:** it follows the latent structured framework of Chang et al. (2010), with ILP inference (Roth and Yih, 2007). It has sentence-relevance variables and cross-turn edge variables.
- **Findings:**
  - Most real objections are overruled, typically raised to interrupt the other side on sensitive topics.
  - Sustained objections are easier to detect than overruled ones.
  - The speaker-situated representation (x^Sit) usually improves predictions and changes which latent variables switch on.

## Interesting ideas
- An action recorded in the text (a judge's ruling) can act as free binary supervision for hidden discourse structure, so no relations need to be annotated by hand.
- The model treats "which sentences matter" and "which cross-turn pairs are connected" as explicit latent decisions solved under ILP constraints, so its output shows exactly which evidence drove the prediction.
- Lexical cues are conditioned on who speaks and on their role and side, because the same words carry different pragmatic weight from a prosecutor than from a witness.
- Hedge/booster and bias lexicons separate evasive or overconfident answers from neutral ones, and loaded word choice ("death" vs. "murder") reveals framing.
- The evaluation splits data by outcome (sustained vs. overruled) and shows that justified cases are easier to detect, so models are judged on clean versus noisy positives.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Mova could mine naturally occurring outcome markers in texts (refusals, corrections, "that's not what I asked") as weak labels for training its speech-act and relation layers.
- **I2** (frames): crate prag could expose an explicit relevance mask plus question→answer links as part of its explainable output, rather than scoring the whole dialogue at once.
- **I3** (frames): implicature and irony detection in prag could key its cue features on the speaker entity and that entity's role in the world of the text.
- **I4** (world): marking each stated fact in the world model with the speaker's certainty and framing would let the reader separate asserted facts from slanted or tentative claims.
- **I5** (learn): evaluations could likewise report results separately on well-grounded and noisy silver examples, which acts as a sanity control on what the model really captures.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Maryland, College Park](https://md.research.elamur.ai/free/en/universities/university-of-maryland-college-park/) — University, College Park, United States · 83 works · 23,784 citations

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## Cite

Cite as: Dan Goldwasser and Hal Daumé III. 2014. “I Object!” Modeling Latent Pragmatic Effects in Courtroom Dialogues. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{goldwasser2014i,
  title = {{“I Object!” Modeling Latent Pragmatic Effects in Courtroom Dialogues}},
  author = {Dan Goldwasser and Hal Daumé III},
  booktitle = {Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics},
  year = {2014},
  doi = {10.3115/v1/e14-1069},
  url = {https://doi.org/10.3115/v1/e14-1069}
}
```

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## References

26 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*