Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies on open-domain conversational systems are limited to single corpus training and evaluation. |
| Approach: | They propose a method which encodes each corpus through a unique corpus embedding and a new word-level importance weighting method that integrates DF to the loss function. |
| Outcome: | The proposed methods gain significant improvements on both automatic and human evaluation. |
Similar Papers
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process . |
How to Encode Domain Information in Relation Classification (2024.lrec-main)
Copied to clipboard
Elisa Bassignana, Viggo Unmack Gascou, Frida Nøhr Laustsen, Gustav Kristensen, Marie Haahr Petersen, Rob van der Goot, Barbara Plank
| Challenge: | Existing deep learning models require a lot of training data to obtain high performance. |
| Approach: | They propose a multi-domain training setup for Relation Classification (RC) they compare different ways to enrich input instances with domain information . |
| Outcome: | The proposed model improves > 2 Macro-F1 against the baseline setup. |
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics (N18-1)
Copied to clipboard
| Challenge: | Existing evaluation metrics for NRG models can't measure semantic relevance and diversity of generated results. |
| Approach: | They propose a large-scale domain-specific conversational corpus with preprocessing and cleansing procedures for model training and a testing set for measuring the diversity of generated results. |
| Outcome: | The proposed corpus can be taken as a new benchmark dataset for the NRG task. |
Multi-domain Tweet Corpora for Sentiment Analysis: Resource Creation and Evaluation (2020.lrec-1)
Copied to clipboard
| Challenge: | a huge amount of content is being generated every day due to the pervasiveness of social media. |
| Approach: | They firstly create a multi-domain tweet sentiment corpora and then establish a deep neural network based baseline framework to address the above mentioned issues. |
| Outcome: | The proposed dataset achieves 84.65% accuracy for sentiment analysis using a neural network, long short term memory, and gated recurrent unit (GRU). |
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models. |
| Approach: | They investigate the impact of parallel corpora quality and quantity, training objectives, and model size on performance of multilingual large language models enhanced with parallel corporeal. |
| Outcome: | The proposed approach improves performance in bilingual and general-purpose tasks. |
What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training (N18-2)
Copied to clipboard
| Challenge: | a key roadblock is application to new domains, unseen in training. |
| Approach: | They propose a method to optimise in- and out-of-domain accuracy by combing domain-specific and domain-general components with adversarial training for domain. |
| Outcome: | The proposed method improves on domain adaptation and domain-adversarial training. |
Revisiting Multi-Domain Machine Translation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
| Approach: | They propose to use domain adaptation methods to handle situations where a sample of matched sentences is available in training and where only samples of source-side sentences are available. |
| Outcome: | The proposed model is able to handle multiple domains and their expectations with respect to performance. |
Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
Copied to clipboard
| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
| Approach: | They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data. |
| Outcome: | The proposed methods improve utterance classification results by drawing on utterant variation. |
Multifaceted Domain-Specific Document Embeddings (2021.naacl-demos)
Copied to clipboard
| Challenge: | Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. |
| Approach: | They propose a faceted domain encoder that transforms each document into a single embedding vector . they use a Siamese neural network architecture to leverage knowledge graphs to enhance the embeddables . |
| Outcome: | The proposed model achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of training data. |
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query. |
| Approach: | They propose to model open-domain dialogue generation using 1-to-1 mapping . they first extract common features of different responses and then combine them with distinctive features to generate multiple diverse and appropriate responses. |
| Outcome: | The proposed model outperforms existing models on automatic and human evaluations. |