[{"id": 617273, "created": "2025-08-08T08:40:33.899343", "project_id": 294, "task_id": 649431, "user_id": 315, "user_ip": null, "finish_time": "2025-08-08T08:41:18.830645", "timeout": null, "calibration": null, "external_uid": null, "media_url": null, "info": {"id": 316, "src": "in this paper , we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering ( q/a ) systems .", "target": "in this paper suggest that current te systems may be able to provide open-domain q / a systems with the forms of semantic inference needed to perform accurate answer .", "clarity": "clear", "faithfulness": "faithful", "plausibility": "plausible", "informativeness": "very_informative"}}, {"id": 626452, "created": "2025-08-18T03:11:09.376898", "project_id": 294, "task_id": 649431, "user_id": 325, "user_ip": null, "finish_time": "2025-08-18T03:11:26.073005", "timeout": null, "calibration": null, "external_uid": null, "media_url": null, "info": {"id": 316, "src": "in this paper , we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering ( q/a ) systems .", "target": "in this paper suggest that current te systems may be able to provide open-domain q / a systems with the forms of semantic inference needed to perform accurate answer .", "clarity": "neutral", "faithfulness": "moderately_faithful", "plausibility": "moderately_plausible", "informativeness": "moderately_informative"}}]