Papers by Daniel Cer
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)
Copied to clipboard
| Challenge: | Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. |
| Approach: | They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references. |
| Outcome: | The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR). |
Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
Copied to clipboard
Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
| Approach: | They propose retrieval focused multilingual sentence embedding models on TensorFlow Hub. |
| Outcome: | The models achieve state-of-the-art on monolingual and cross-lingual retrieval (SR) and retrieval question answering (ReQA) competitive performance is obtained on related tasks of translation pair bitext retrieval and retrieving question answering. |
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that pre-trained language models can be more efficient when they are larger than they are in their size. |
| Approach: | They propose a prompt-based transfer learning approach called SPoT: Soft Prompt Transfer that learns a soft prompt on one or more source tasks and initializes it for a target task. |
| Outcome: | The proposed approach outperforms Prompt Tuning and MODELTUNING on superGLUE benchmarks while using up to 27,000 fewer task-specific parameters. |
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
Copied to clipboard
| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
| Outcome: | The proposed models outperform the previous best models on both SentEval and SentGLUE transfer tasks. |
Universal Sentence Encoder for English (D18-2)
Copied to clipboard
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil
| Challenge: | TensorFlow Hub sentence embedding models have good task transfer performance . model variants allow for trade-offs between accuracy and compute resources . |
| Approach: | They propose easy-to-use TensorFlow Hub sentence embedding models with good task transfer performance. |
| Outcome: | The proposed models outperform models without transfer learning and those that use only word-level transfer on a number of NLP tasks. |
Universal Sentence Representation Learning with Conditional Masked Language Model (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to learn sentence representations on unlabeled corpora are difficult and expensive to obtain, making it hard to cover many domains and languages. |
| Approach: | They propose a method to train sentence representations on large unlabeled corpora by conditioning on the encoded vectors of adjacent sentences. |
| Outcome: | The proposed method outperforms existing models on SentEval and can be extended to a broad range of languages and domains. |
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing image captioning datasets have limited cross-modal associations, preventing researchers from examining how inter-modal learning impacts intra-modal tasks. |
| Approach: | They propose to use image captioning data to support multi-modal retrieval training and evaluation to assess the impact of inter-modality learning. |
| Outcome: | The proposed model is able to measure the influence of intra- and inter-modality learning. |
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)
Copied to clipboard
| Challenge: | et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages. |
| Approach: | They propose a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual retrievers without human supervision. |
| Outcome: | The proposed model outperforms human-supervised retrieval models on three retrieval benchmarks. |
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
Copied to clipboard
| Challenge: | Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem . |
| Approach: | They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques . |
| Outcome: | The proposed model outperforms human models on identifying answer spans within a specific passage . the proposed model is scalable and can bypass the typical document retrieval step . |
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | generative multilingual models fine-tuned on English forget to generate non-English data when labeled data is only available in English . generative models fine tuned on English fail to generate multilingual summarization tasks when labeling data is available in other languages . |
| Approach: | They propose to use prompt tuning to overcome catastrophic forgetting in a generative task in . they assume a strict setting with no parallel data or machine translation . |
| Outcome: | The proposed method can overcome catastrophic forgetting to enable zero-shot cross-lingual generation. |
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Language agnostic and semantic-language information isolation is an emerging research direction for multilingual representations models. |
| Approach: | They propose a method that factors out language identity information from semantic related components in multilingual representations pre-trained on monolingual data. |
| Outcome: | The proposed method improves cross-lingual transfer performance on weak alignment models. |
Language-agnostic BERT Sentence Embedding (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for learning bilingual sentence embeddings are not well explored. |
| Approach: | They propose to combine best methods for learning multilingual sentence embeddings with pre-trained models to achieve 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba. |
| Outcome: | The proposed model achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, above the 65.5% achieved by LASER. |