Lexical based two-way RTE System at RTE-5.
Bibliographic record
Abstract
The note describes the lexical based two-way Recognizing Textual Entailment (RTE) system developed at the Computer Science and Engineering Department, Jadavpur University, India. We participated in the two-way main task at RTE-5. The system is based on the composition of the following six lexical based RTE methods: WordNet based unigram match, bigram match, longest common sub-sequence, skip-gram, stemming and named entity matching. Each of these methods were applied on the development data to obtain two-way decisions. It was observed on the development data that final entailment decision on a text-hypothesis pair that is based on positive entailment decisions from at least two lexical based RTE methods was producing a better precision and recall figure. An accuracy figure of 58.17% was obtained on the test data. Ablation tests were performed for each of the six RTE methods and these are reported in the present note. The RTE task was based on three application settings: QA, IE and IR but this information was not taken into consideration during the system development. The relatively higher accuracy figures for the IR application setting obtained during the various tests suggest that identification of appropriate RTE methods based on the application settings might have improved the accuracy scores further.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".