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Same Author or Just Same Topic? Towards Content-Independent Style Representations

Anna Wegmann, Marijn Schraagen, Dong Nguyen

arXiv:2204.04907v1cs.CL

TL;DR

Authorship-verification training can learn style from author labels, but style and content correlations may let representations capture topic cues instead. The paper adds contrastive training and controls content with conversation or domain proxies, finding conversation control better at representing style independently of content. The approach is bounded by the difficulty of transferring conversation-based control beyond conversation datasets and by a potential triplet-problem limitation in STEL-Or-Content.

  • Problem

    AV-trained representations may encode spurious content correlations because style and topic often correlate, complicating the learning of general-purpose style representations.

    Source: §1 · p. 1

  • Method

    The paper varies AV with a contrastive utterance and controls content using conversation or domain proxies, evaluating learned style representations with an adaptation of STEL.

    Source: §1 · p. 2

  • Results

    Representations trained with conversation content control better represent style independently of content than those trained with domain or no content control.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Conversation is a more effective topic proxy than domain labels or no control for learning content-independent style representations in the evaluated setting.

    Source: §1 · p. 2

    Conversation-based content control may be difficult to transfer beyond conversation datasets, and STEL-Or-Content retains a potential triplet-problem limitation.

    Source: §6 · p. 9

Abstract

from arXiv · show

Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good "general-purpose" style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.

1 Introduction

Authorship-verification training can scale style learning through author labels, but style and topic often correlate, so models may learn content cues instead of style. The paper introduces contrastive and content-controlled training variations and finds conversation-based control best represents style independently of content.

1 Introduction

  • Style has applications in language understanding and generation, stylometric analysis, author profiling, and style preservation in machine translation.The paper frames linguistic style as how something is said.Source: §1 · p. 1
  • AV-based training uses author identifiers to learn style representations without additional labels, enabling extensive self-supervised training.This relies on the assumption that texts by the same author approximate the same writing style.Source: §1 · p. 1
  • Style and content often correlate, so AV-trained representations without content control may encode spurious content correlations.For example, writing about professional careers may be more formal than writing about personal hobbies.Source: §1 · p. 1
  • The paper distinguishes content, where utterances are paraphrases, from topic, where utterances may discuss the same subject without expressing the same content.Existing methods use no or limited content control, or domain labels that may be too coarse-grained to represent content fully.Source: §1 · p. 2
  • The approach adds a contrastive utterance and controls content using conversation or domain labels, then evaluates style dimensions, content preference, and same-author similarity across differing content.The paper adapts the STEL framework to test whether representations prefer style over content.Source: Figure 1 · p. 1, §1 · p. 2
  • Conversation-based content control yields better content-independent style representations than domain-based or uncontrolled training, and performs better with CAV than AV.The paper also demonstrates stylistic distinctions such as punctuation and apostrophe types through clustering.Source: §1 · p. 2

2 Related Work

Prior work uses authorship verification and semantic-embedding objectives to train and evaluate representations, while content control in style learning remains limited. The related work also notes that common candidate features for content-independent authorship verification may themselves carry content information.

2 Related Work

  • Transformer-based approaches have been applied to authorship verification, and BERT has performed competitively in AV and style representation learning.Prior content controls include domain sampling and restricting the feature space, but even function words and character n-grams may not be content-independent.Source: §2 · pp. 2–3
  • Semantic representations use supervised natural-language-inference data or self-supervised contrastive objectives, and may be evaluated on semantic similarity or downstream tasks.Triplet loss pulls an anchor toward a semantically close sentence and away from a distant one.Source: §2 · pp. 2–3
  • Authorship-verification methods have used domain sampling to address spurious correlations, while style representations are often trained and evaluated on AV.Earlier approaches also restrict features to function words or character n-grams, though such features are not necessarily content-independent.Source: §2 · pp. 2–3

3 Style Representation Learning

The paper adapts authorship verification with a contrastive three-utterance task and topic-proxy content control, then generates Reddit tasks and fine-tunes siamese transformer networks. Its training design compares AV and CAV objectives and conversation, domain, and absent content control.

3.1 Training Task

  • CAV asks which of two utterances shares an author with an anchor, adding a contrastive different-author utterance to the usual AV task.The added contrast enables objectives that take three sentences as input; the paper evaluates both AV and CAV setups.Source: §3.1 · p. 3
  • Content control samples the different-author utterance from the same conversation, same domain, or randomly as a no-control baseline.Conversation is proposed as a finer topic proxy to compare with domain labels.Source: §3.1 · p. 3

3.2 Task Generation

  • The Reddit sample contains conversations from 100 active subreddits, with 600 conversations sampled per subreddit and at least 10 posts (called utterances) in each conversation.Source: §3.2 · p. 3
  • Training and evaluation tasks use a 70/15/15 author split across conversation, domain, and no-content-control levels, with distinct CAV triples and corresponding AV pairs.For domain and no-control conditions, the different-author utterance is resampled while the anchor and same-author utterance are reused; sampled same-author pairs may repeat, but the share remains below 1%.Source: §3.2 · p. 4

3.3 Training

  • The authors fine-tune siamese BERT and RoBERTa models using contrastive loss for AV and triplet loss for CAV, selecting each best checkpoint with the corresponding setup-specific evaluator.Training uses batch size 8 for 4 epochs, with 10% of training data as warm-up steps and Adam at the default learning rate of 0.00002.Source: §3.3 · p. 4

4 Evaluation

The evaluation tests authorship verification, known style dimensions, and whether representations prefer style to content. Conversation-controlled CAV performs best on content-independent style, while AV or STEL-Or-Content scores alone require cautious interpretation.

4 Evaluation

  • The evaluation proceeds from AV performance to known style dimensions in STEL, then tests content independence by manipulating STEL instances to pit style against content cues.In STEL-Or-Content, a same-content, different-style sentence competes with a same-style, different-content sentence; STEL uses paraphrases to control content.Source: §4 · p. 5, §4.2 · p. 6, §4.3 · p. 7

4.1 Authorship Verification

  • AV and CAV training produced similar test performance, while representations generally did best when tested at their training content-control level.The conversation test set was hardest, consistent with its semantically closer different-author pairs; AV performance alone may reflect content rather than style.Source: §4.1 · pp. 5–6

4.2 STEL Task

  • Fine-tuned models were comparable across STEL setups and content-control levels, but performance stayed similar or improved on formal/informal and contraction dimensions while dropping on complex/simple and nb3r substitution.Ambiguous examples were more common among unlearned than newly learned instances (50/55 versus 29/41); the nb3r dimension contains 100 instances.Source: §4.2 · p. 6

4.3 Content-Independence of Style Representations

  • Accuracy rose from 0.05 for original RoBERTa to up to 0.42±.01 for CAV with conversation CC; this representation also performed comparably on AV test sets and the STEL framework.STEL-Or-Content performance was below 0.5, its random baseline, and the authors say to consider it alongside AV and STEL because models may succeed by punishing same-content cues.Source: Table 3 · p. 7, §4.3 · pp. 7–8

5 Style Representation Analysis

Clustering the best-performing conversation-controlled CAV representation revealed stylistic regularities, especially in punctuation, casing, and spelling. Its clusters differed from base RoBERTa clusters, which mainly separated utterance length and hyperlinks.

5 Style Representation Analysis

  • Among same-author utterance pairs, 46.2% fell in the same cluster, versus 20.1% ± .00 for random assignments across seven clusters.The analysis clustered 14,756 unique utterances from 5,000 conversation-test tasks using the best-performing CAV representation.Source: §5 · p. 8
  • Clusters showed consistencies in punctuation, casing, contraction spelling, apostrophe type, and line breaks, though the manual inspection mostly found letter-level patterns.Examples include no final punctuation in 97% of Cluster 3 utterances and line breaks in 72% of Cluster 7 utterances.Source: §5 · p. 8
  • Base RoBERTa clusters mainly separated utterance length and hyperlink presence, unlike the trained style representation’s less clearly separated lengths.The comparison concerns three base-model clusters containing more than three elements and not as many as 86.7% of utterances.Source: §5 · pp. 8–9

6 Limitations and Future Work

The paper identifies limits in transferring conversation-based content control, in STEL-Or-Content comparisons, and in the kinds of stylistic variation the models may learn.

6 Limitations and Future Work

  • Conversation-based content control is difficult to transfer beyond conversation datasets, and content can still help distinguish authors within a conversation.The authors suggest semantic sentence similarity as a possible basis for labeling utterances with the same content.Source: §6 · p. 9
  • In STEL-Or-Content, the triplet setup can rank an utterance as less informal without ensuring it is stylistically farther from the anchor.The authors expect this problematic case to be less prevalent, but note it could impose a performance cap.Source: §6 · p. 9
  • Positive same-author pairs may capture individual stylistic variation, while training across thousands of authors may also capture styles shared by groups.The authors suggest exploring how training tasks and content-control levels influence which styles are learned.Source: §6 · p. 9

7 Conclusion

The paper introduces content control using conversation or domain membership, a contrastive authorship-verification setup, and a STEL adaptation to assess style preference. Conversation-controlled CAV representations are reported to be more independent of content than alternatives, and clustering illustrates stylistic features they capture.

7 Conclusion

  • Conversation-controlled CAV representations represent style more independently from content than models using other content-control levels or the AV setup.Combining the conversation topic proxy with CAV also outperforms combining it with AV.Source: §7 · p. 9
  • The paper trains style representations with conversation or domain content control and compares the new Contrastive Authorship Verification setup with standard AV.An adaptation of STEL tests whether learned representations favor style over content.Source: §7 · p. 9
  • Agglomerative clustering reveals stylistic differences such as using a right single quotation mark rather than an apostrophe in contractions.Source: §7 · p. 9

Ethical Considerations

The ethical discussion qualifies the Reddit data's representativeness and privacy implications, while the hyperparameter tuning reports broadly comparable contrastive and triplet losses and favors RoBERTa for the main experiments.

Ethical Considerations

  • The 100-subreddit Reddit sample may not represent English use across social groups because its user demographics are likely skewed.The authors also note potential overrepresentation of locally based subreddits and that average Reddit users are more likely to be young and male.Source: Ethical Considerations · pp. 9–10
  • Reusing the Reddit dataset may increase visibility of potentially privacy-infringing data, though aggregated representations are not expected to indicate individual users.Source: Ethical Considerations · p. 10

A.1 Hyperparameter Tuning

  • Contrastive and triplet losses performed comparably, while margin values had only a small influence on performance.The authors selected a margin of 0.5 and used contrastive and triplet losses in further models.Source: §A.1 · p. 14, Table 6 · p. 14
  • Models performed best on the task they were trained on, and CAV accuracies exceeded AV accuracies in the reported development results.Source: Table 7 · p. 15

A.2 Detailed Results on the Development Sets

The development results report stronger general performance for RoBERTa than BERT and broadly comparable contrastive and triplet losses. STEL-Or-Content performance generally increases, although trained models decline on two original STEL dimensions.

A.2 Detailed Results on the Development Sets

  • RoBERTa generally performs better than uncased and cased BERT, while triplet and contrastive loss performances are comparable.The main paper therefore uses RoBERTa models with both loss functions.Source: §A.2 · p. 15
  • Performance for trained models decreases on the original STEL complex/simple and nb3r substitution dimensions.Source: Table 8 · p. 16
  • Performance generally increases for the STEL-Or-Content task.Source: Table 8 · p. 16

B Details on STEL results

Appendix STEL analyses report a cased-BERT advantage on the contraction dimension and examine ambiguity in learned RoBERTa STEL instances.

B Details on STEL results

B.1 Error Analysis RoBERTa STEL results

  • The error analysis manually grouped common learned and unlearned simple and complex examples into ambiguity classes.Source: Table 10 · p. 17
  • Learned STEL instances contain fewer ambiguities overall, although considerable ambiguity remains.Source: §B.1 · p. 17, Table 9 · p. 17

C Details on cluster parameters

The cluster-parameter analysis uses a conversation-controlled CAV RoBERTa model and selects seven clusters after comparing silhouette scores.

C Details on cluster parameters

D Details on the cluster analysis

The cluster analysis compares same-author, same-domain, and same-conversation pairings against random assignments, then describes the analysis limits, computing work, and intended reuse.

D Details on the cluster analysis

E Computing Infrastructure

  • Training 23 RoBERTa, 13 uncased BERT, and 6 cased BERT models took about 846 GPU hours; further analysis of two RoBERTa models took about 24 GPU hours.Source: E Computing Infrastructure · p. 20
  • The authors say they used prior work, code, and data according to intended-use terms, obtained permission for part of the STEL dataset, and cited specified works.Source: E Computing Infrastructure · p. 20

F Intended Use

  • The authors invite researchers to reuse their results, code, and data for further work on style and its representations.Source: F Intended Use · p. 20

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