Papers with fixed-length encodings
Sparse, Dense, and Attentional Representations for Text Retrieval (2021.tacl-1)
Copied to clipboard
| Challenge: | Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. |
| Approach: | They propose a dual-encoder-based neural model that combines the efficiency of dual encoders with expressiveness of more costly attentional architectures. |
| Outcome: | The proposed model outperforms strong alternatives in large-scale retrieval. |
T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text (2024.acl-long)
Copied to clipboard
| Challenge: | Existing vector quantization methods are fixed-length encodings, overlooking the uneven information density in sign language. |
| Approach: | They propose a two-stage sign language production paradigm that encodes sign language sequences into discrete codes and autoregressively generates sign languages from text. |
| Outcome: | The proposed model can dynamically adjust the encoding length based on the information density in sign language to achieve accurate and compact encoded enccoding. |