Enterprise AI Analysis
From Lemmas to Dependencies: What Signals Drive Light Verbs Classification?
This paper investigates the key linguistic signals driving Light Verb Construction (LVC) classification in Turkish, a language with complex morphology. By systematically restricting model inputs, the authors compare lemma-driven, grammar-only, and full-input BERTurk models. The core finding is that coarse morphosyntactic information alone is insufficient for robust LVC detection, especially under controlled literal-idiomatic contrasts. Lexical identity, particularly lemma-level information, is crucial but highly sensitive to normalization and calibration. The study highlights that 'lemma-only' is not a singular representation, but rather a family affected by operationalization, leading to distribution shifts. The work emphasizes the need for targeted diagnostic evaluation in MWE research.
Executive Impact & Key Findings
Understanding the specific signals that drive language model performance in complex linguistic tasks is crucial for developing more robust and reliable AI systems. This research offers critical insights into the limitations of current approaches and paths for future development, particularly for morphologically rich languages like Turkish.
0
LVC Recall (Full BERTurk 128K)
0
Overall Accuracy (Full BERTurk 128K)
0
Grammar-Only LVC Recall
0
Lemma-Only (Surface-Test) LVC Recall
Deep Analysis & Enterprise Applications
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Lexical vs. Morphosyntactic Evidence
Normalization & Distribution Shift
Diagnostic Evaluation & MWEs
This section delves into the ongoing debate regarding whether evidence for Multiword Expressions (MWEs) resides in lexical identity (lemmas) or morphosyntactic configuration (POS/morphology and dependency structure). The paper tests this by creating models with restricted inputs to isolate the contribution of each type of information.
10.2%
LVC Recall with Grammar-Only LR
91.8%
Grammar-Only NLVC Accuracy
Grammar-Only Limitations
The grammar-only Logistic Regression model, relying solely on UPOS/DEPREL/MORPH features, performs near-perfectly on general negatives and NLVC lexical controls. However, its performance collapses dramatically on LVC positives, indicating that coarse morphosyntactic signals alone are insufficient to distinguish idiomatic LVCs from literal verb-argument uses under controlled contrasts.
Lexical Identity's Role
Lemma-level lexical identity provides substantially stronger evidence for LVC status. Models trained on lemma sequences (e.g., Lemma-only BERTurk) perform significantly better on LVC positives than grammar-only models, demonstrating the critical importance of specific lexical content for identifying conventionalized predicate meanings.
The research highlights that 'lemma-only' is not a single, well-defined representation but a family of representations critically dependent on how normalization is operationalized. This leads to meaningful distribution shifts, affecting model behavior at test time.
71.4%
Lemma-Only BERTurk (128K) LVC Recall (Surface-Test)
36.7%
Lemma-Only BERTurk (128K) LVC Recall (Lemma-Test)
Impact of Test-Time Input Form
Lemma-only BERTurk models show a strong dependence on the test-time input form. When evaluated on original surface sentences, performance is comparatively strong. However, when evaluated on lemmatized versions of the diagnostic set (lemma-test), LVC accuracy drops sharply. This asymmetry suggests distribution shifts arise from mismatches in lemma inventory or tokenization during automatic preprocessing versus training data.
Vocabulary Size and Robustness
The 128K subword vocabulary BERTurk model degrades less under lemma-test conditions compared to the 32K model. This suggests that a larger subword vocabulary can reduce token fragmentation and improve robustness to normalization or lemma-form mismatches, making the model more resilient to variations introduced by lemmatization pipelines.
The paper advocates for targeted diagnostic evaluations and split-wise reporting for Multiword Expressions (MWEs), moving beyond single pooled scores to better understand model capabilities and limitations.
147
Total Diagnostic Sentences
3
Balanced Conditions
Controlled Diagnostic Evaluation Setup
RANDOM Negatives (no LVCs, different lemmas)
→
NLVC Lexical Controls (literal, same lemmas)
→
LVC Positives (idiomatic, same lemmas)
Limitations of Pooled Accuracy
The diagnostic evaluation highlights that pooled accuracy can mask systematic misses of LVCs under a conservative negative bias. Split-wise reporting (Random/NLVC/LVC) is crucial for exposing decision-boundary behavior and understanding model performance in nuanced literal-idiomatic contrasts.
Relevance for Morphologically Rich Languages
The findings position Turkish LVCs as a useful probe for separating lexicalized predicate meaning from surface argument structure in morphologically rich languages, emphasizing the need for robust handling of inflectional and derivational variation in MWE identification.
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