Search Results for author:
Found 12 papers, 2 papers with code
Gecko: Versatile Text Embeddings Distilled from Large Language Models
On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size.
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many information retrieval benchmarks.
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models
Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance.
How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?
Sequence labeling is a core task in text understanding for IE/IR systems.
Multi-Vector Retrieval as Sparse Alignment
With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.
Transforming Sequence Tagging Into A Seq2Seq Task
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks.
Feature-Based Decipherment for Machine Translation
The existing decipherment models, however, are not well suited for exploiting these orthographic similarities.
Feature-based Decipherment for Large Vocabulary Machine Translation
Our results show that the proposed log-linear model with contrastive divergence scales to large vocabularies and outperforms the existing generative decipherment models by exploiting the orthographic features.
Automated Analysis and Prediction of Job Interview Performance
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews.
