Conversational Query Reformulation (CQR) is an important component in Conversational Question Answering (ConvQA), where user queries are often incomplete, ambiguous, and dependent on previous dialogue turns. Recent CQR approaches have shown the effectiveness of large language models (LLMs) in generating standalone queries. However, rewritten queries do not always lead to optimal retrieval performance. This paper presents a retrieval-aware CQR framework that integrates fine-grained retrieval feedback and keyword augmentation to improve downstream document retrieval. Instead of treating the LLM-rewritten query as the final search query, our framework uses it to retrieve candidate documents, identify relevant paragraph-level evidence, generate a pseudo-answer, and extract semantically relevant keywords. These keywords are then filtered based on their alignment with the reformulated query and dialogue history before being incorporated into the final query. We evaluate the proposed framework on a sampled subset of the QReCC dataset using BM25 and GTR as sparse and dense retrieval backbones. The experimental results show that our proposed approach improves retrieval accuracy compared to the baseline model across all metrics such as Recall@10, MRR and NDCG. Specifically, under the BM25 sparse retrieval framework, our best-performing configuration achieves improvements in Recall@10 (81.29), MRR (56.06) and NDCG (60.92) compared to the baseline. A comparable pattern of substantial gains holds under the GTR dense retrieval framework, achieving a Recall@10 (76.51), MRR (53.26) and NDCG (57.54), indicating the effectiveness of our method across different retrieval paradigms.
Andhika Putra Bagaskara, Arie Ardiyanti Suryani· International Conference on...· 0 citations
As the amount of text and image data on social media continues to increase, multimodal sentiment analysis has emerged as a critical area of study. However, a "modality gap" that lowers classification accuracy is frequently caused by the domain disparities between textual and visual modalities. In this paper, a Translation Alignment method is used to improve a sentiment analysis system. Using three tools, first image captioning (BLIP), second facial expression identification (InsightFace), and last optical character recognition (EasyOCR), this method aligns the modalities by translating image information into textual descriptions. This transformation allows visual information to be represented in the same textual domain as the original post, reducing the semantic distance between modalities. In addition, the translated visual cues provide complementary information such as scene context, emotional expressions, and embedded text that may not be fully captured by the original caption alone. Using an Early Fusion strategy, the translated textual outputs from BLIP, InsightFace, and EasyOCR are concatenated with the original text prior to encoding, enabling the BERT-POS-LSTM architecture to process a unified multimodal textual representation. To rectify the MVSA dataset's imbalance, the SMOTE technique was applied to the latent feature space during training. The suggested model obtains an average accuracy of 73.7% and an F1 score of 72.6%, according to experimental results. These findings confirm that text-based domain alignment offers a more comprehensive and effective representation for multimodal sentiment analysis.