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Found 40 papers, 14 papers with code
MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering Models
Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This dataset paper presents MultiReQA, a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets.
Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems
Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages.
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.
Gemma: Open Models Based on Gemini Research and Technology
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models.
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
There has been limited success for dense retrieval models in multilingual retrieval, due to uneven and scarce training data available across multiple languages.
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts
The resulting soft knowledge prompts (KPs) are task independent and work as an external memory of the LMs.
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
In this paper, we explore the challenging problem of performing a generative task in a target language when labeled data is only available in English, using summarization as a case study.
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer
Finally, we propose an efficient retrieval approach that interprets task prompts as task embeddings to identify similar tasks and predict the most transferable source tasks for a novel target task.
A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
A simple but highly effective method "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data.
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models
To support our investigation, we establish a new sentence representation transfer benchmark, SentGLUE, which extends the SentEval toolkit to nine tasks from the GLUE benchmark.
NT5?! Training T5 to Perform Numerical Reasoning
Numerical reasoning over text (NRoT) presents unique challenges that are not well addressed by existing pre-training objectives.
Universal Sentence Representation Learning with Conditional Masked Language Model
This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
We explore using T5 (Raffel et al. (2019)) to directly translate natural language questions into SQL statements.
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation
Independently computing embeddings for questions and answers results in late fusion of information related to matching questions to their answers.
Language-agnostic BERT Sentence Embedding
While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.
MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets.
Crisscrossed Captions: Extended Intramodal and Intermodal Semantic Similarity Judgments for MS-COCO
By supporting multi-modal retrieval training and evaluation, image captioning datasets have spurred remarkable progress on representation learning.
ReQA: An Evaluation for End-to-End Answer Retrieval Models
Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance.
Multilingual Universal Sentence Encoder for Semantic Retrieval
We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures.
Hierarchical Document Encoder for Parallel Corpus Mining
We explore using multilingual document embeddings for nearest neighbor mining of parallel data.
Universal Sentence Encoder for English
We present easy-to-use TensorFlow Hub sentence embedding models having good task transfer performance.
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data.
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings.
Learning Semantic Textual Similarity from Conversations
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data.
Universal Sentence Encoder
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
Ranked #2 on
Text Classification
on TREC-6
Conversational Response Selection
Semantic Textual Similarity
+7
SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Semantic Textual Similarity (STS) measures the meaning similarity of sentences.
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
Semantic Textual Similarity (STS) measures the meaning similarity of sentences.
