A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding (N18-3)
Copied to clipboard
| Challenge: | Existing approaches to classify a given utterance into domains are costly and time-consuming. |
| Approach: | They propose a shortlisting-reranking neural model for large-scale domain classification for IPDAs . they use extensive experiments on 1,500 IPDA domains to test their effectiveness . |
| Outcome: | The proposed model is tested on 1,500 IPDA domains. |
Similar Papers
Efficient Large-Scale Neural Domain Classification with Personalized Attention (P18-1)
Copied to clipboard
| Challenge: | Using a scalable neural model, we show that personalization improves domain classification accuracy in a setting with thousands of overlapping domains. |
| Approach: | They propose a scalable neural model architecture with a shared encoder that incorporates personalization information and domain-specific classifiers that solves the problem efficiently. |
| Outcome: | The proposed architecture achieves two orders of magnitude faster than full model retraining. |
Continuous Learning for Large-scale Personalized Domain Classification (N19-1)
Copied to clipboard
| Challenge: | Domain classification is the task to map spoken language utterances to one of the natural language understanding domains in intelligent personal digital assistants. |
| Approach: | They propose a neural-based approach for continuous domain adaption with normalization and regularization to accommodate new domains. |
| Outcome: | The proposed approach outperforms baseline methods on accommodated new domains and existing known domains by a large margin. |
Discriminative Reranking for Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | reranking models allow the integration of rich features to select a better output hypothesis within an n-best list or lattice. |
| Approach: | They use discriminative reranking to train a large transformer architecture to train an ranked list of hypotheses. |
| Outcome: | Experiments on four WMT directions show that discriminative reranking improves translation quality. |
Reranking for Neural Semantic Parsing (P19-1)
Copied to clipboard
| Challenge: | Semantic parsing is the task of transducing natural language utterances into machine executable meaning representations (e.g., Python code). |
| Approach: | They propose to rerank an n-best list of predicted MRs and use features to fix observed problems with baseline models to improve parser performance. |
| Outcome: | The proposed method outperforms the best published neural parser on four datasets and improves the baseline parsing performance by 5.7% and 2.9%. |
FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing listwise LLMs lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. |
| Approach: | They propose a listwise LLM reranking approach that leverages the first generated identifier to obtain a ranked ordering of the candidates. |
| Outcome: | The proposed approach accelerates inference by 50% while maintaining robust ranking performance with gains across BEIR benchmark. |
Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser. |
| Approach: | They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies . |
| Outcome: | The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data. |
Methods, Applications, and Directions of Learning-to-Rank in NLP Research (2024.findings-naacl)
Copied to clipboard
| Challenge: | Learning-to-rank (LTR) algorithms aim to order items according to some criteria. |
| Approach: | They focus on the formal background of LTR and the most widely-used supervised methods . they also discuss how large language models are changing the LTR landscape . |
| Outcome: | The proposed methods are used in natural language processing and information retrieval tasks. |
Lightweight reranking for language model generations (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can exhibit considerable variation in quality of sampled outputs. |
| Approach: | They propose a method for reranking LLM generations using pairwise statistics . they show strong improvements for selecting the best k generations for code generation tasks . |
| Outcome: | The proposed approach improves selection and generation quality for code generation tasks and autoformalization, summarization, and translation tasks. |
Make Large Language Model a Better Ranker (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) demonstrate robust capabilities across various fields . current list-wise approaches fail in ranking tasks due to misalignment between ranking objectives and next-token prediction . |
| Approach: | They propose a large language model framework with Aligned Listwise Ranking Objectives (ALRO) this framework provides explicit feedback in a listwise manner by introducing soft lambda loss . |
| Outcome: | The proposed model outperforms existing recommendation methods and embedding-based recommendations without additional computational burdens. |
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents (N18-3)
Copied to clipboard
| Challenge: | a recent paper describes efficient deep neural network architectures for expanding natural language capabilities of virtual agents. |
| Approach: | They propose deep neural network architectures that maximize re-use available resources . they use data from Amazon Alexa to accelerate expansion of new natural language domains . |
| Outcome: | The proposed methods increase accuracy in low resource settings and enable rapid development with less data. |