Slot Filling

31 papers with code · Natural Language Processing

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

Benchmarks

Greatest papers with code

Learning End-to-End Goal-Oriented Dialog

24 May 2016facebookresearch/ParlAI

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

NeurIPS 2016 HazyResearch/snorkel

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

Neural Baby Talk

CVPR 2018 jiasenlu/NeuralBabyTalk

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

KILT: a Benchmark for Knowledge Intensive Language Tasks

4 Sep 2020facebookresearch/KILT

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

ENTITY LINKING OPEN-DOMAIN QUESTION ANSWERING SLOT FILLING

Position-aware Attention and Supervised Data Improve Slot Filling

EMNLP 2017 yuhaozhang/tacred-relation

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

KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING

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

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

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

DIALOGUE STATE TRACKING SLOT FILLING

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

NAACL 2018 MiuLab/SlotGated-SLU

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.

INTENT DETECTION SLOT FILLING SPOKEN DIALOGUE SYSTEMS SPOKEN LANGUAGE UNDERSTANDING

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

6 Sep 2016DSKSD/RNN-for-Joint-NLU

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.

INTENT CLASSIFICATION INTENT DETECTION SLOT FILLING

A Knowledge-Grounded Neural Conversation Model

7 Feb 2017DSTC-MSR-NLP/DSTC7-End-to-End-Conversation-Modeling

We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.

SLOT FILLING