Slot Filling

75 papers with code • 7 benchmarks • 13 datasets

The goal of Slot Filling is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.

Source: Real-time On-Demand Crowd-powered Entity Extraction

Image credit: Robust Retrieval Augmented Generation for Zero-shot Slot Filling

Greatest papers with code

Learning End-to-End Goal-Oriented Dialog

facebookresearch/ParlAI 24 May 2016

We show similar result patterns on data extracted from an online concierge service.

Goal-Oriented Dialog Slot Filling

Data Programming: Creating Large Training Sets, Quickly

HazyResearch/snorkel NeurIPS 2016

Additionally, in initial user studies we observed that data programming may be an easier way for non-experts to create machine learning models when training data is limited or unavailable.

Slot Filling

Learning Dense Representations of Phrases at Scale

princeton-nlp/SimCSE ACL 2021

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).

Fine-tuning Open-Domain Question Answering +4

SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising

salesforce/WikiSQL 17 May 2021

In text-to-SQL task, seq-to-seq models often lead to sub-optimal performance due to limitations in their architecture.

Denoising Slot Filling +1

KILT: a Benchmark for Knowledge Intensive Language Tasks

facebookresearch/KILT NAACL 2021

We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.

Entity Linking Fact Checking +4

Neural Baby Talk

jiasenlu/NeuralBabyTalk CVPR 2018

We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image.

Image Captioning Slot Filling

Phrase Retrieval Learns Passage Retrieval, Too

princeton-nlp/DensePhrases EMNLP 2021

Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems.

Entity Linking Open-Domain Question Answering +3

Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset

google-research-datasets/dstc8-schema-guided-dialogue 12 Sep 2019

In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.

Dialogue State Tracking Language understanding +1

BERT for Joint Intent Classification and Slot Filling

monologg/JointBERT 28 Feb 2019

Intent classification and slot filling are two essential tasks for natural language understanding.

Classification Fine-tuning +5

Position-aware Attention and Supervised Data Improve Slot Filling

yuhaozhang/tacred-relation EMNLP 2017

The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.

Knowledge Base Population Knowledge Graphs +2