Papers with latent variable model
Cascaded Head-colliding Attention (2021.acl-long)
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| Challenge: | Existing frameworks for natural language processing ignore interactions among different heads, which wastes the capacity of the model. |
| Approach: | They propose a model which explicitly models interactions between attention heads through a hierarchical variational distribution. |
| Outcome: | The proposed model outperforms the baseline model on Wikitext-103 and WMT14 EN-DE on language modeling and translation tasks. |
Latent Part-of-Speech Sequences for Neural Machine Translation (D19-1)
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| Challenge: | Existing methods for learning target side syntactic structure are greedy and only allow them to explore a limited portion of the latent space. |
| Approach: | They propose a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics while allowing for effective inference over the latent space. |
| Outcome: | The proposed model captures the co-dependence between syntax and semantics while allowing for efficient inference over the latent space. |
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables (D19-1)
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| Challenge: | a lack of research on multilingual or cross-lingual task-oriented dialog systems has limited results . we propose a zero-shot adaptation of task-orientated dialog systems to low-resource languages . task-focused systems are often trained with monolingual datasets that are expensive to build or acquire . |
| Approach: | They propose a zero-shot adaptation of multilingual task-oriented dialog systems to low-resource languages using latent variables and a set of very few parallel word pairs. |
| Outcome: | The proposed model performs better in natural language understanding task compared to state-of-the-art model . the proposed model uses very few parallel word pairs to refine cross-lingual representations . |
Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)
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| Challenge: | Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive. |
| Approach: | They propose to query a masked language model with cloze style prompts to obtain supervision signals. |
| Outcome: | The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%. |
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference (P19-1)
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| Challenge: | Supervised NLP models rely on large collections of text which closely resemble intended testing setting. however, data is often messy, with domain labels not always available, or providing limited information about the style and genre of text. |
| Approach: | They propose a method to distill the important domain signal as part of a multi-domain learning system using a latent variable model. |
| Outcome: | The proposed model improves performance over benchmark domain adaptation methods . text corpora are often collated from several different sources, including news, literature, microblogs, and web crawls . |
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information . |
| Approach: | They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection. |
| Outcome: | The proposed model improves on two knowledge-grounded dialogue datasets. |
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching (P19-1)
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| Challenge: | Existing approaches to text matching consider each sequence separately . a proposed model uses both sequences to generate a given relationship with a source sequence . |
| Approach: | They propose a latent variable model for predicting the relationship between a pair of text sequences by generating a sequence that has a given relationship with a source sequence. |
| Outcome: | The proposed model achieves state-of-the-art on natural language inference and paraphrase identification. |
Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)
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| Challenge: | despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal. |
| Approach: | They propose a regularization approach to align word-level and sentence-level representations across languages without external resources. |
| Outcome: | The proposed model outperforms state-of-the-art models in few-shot and zero-shot scenarios and achieves comparable performance to supervised training with all training data. |
Latent Variable Model for Multi-modal Translation (P19-1)
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| Challenge: | Libovick and Helcl (2017) show improvements due to imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable and training on additional target-language image descriptions. |
| Approach: | They propose to model interaction between visual and textual features for multi-modal neural machine translation (MMT) using a latent variable model. |
| Outcome: | The proposed model improves over baselines including a multi-task learning approach and a conditional variational auto-encoder approach. |
Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction (2023.findings-acl)
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| Challenge: | a novel latent variable model for fact-checking and analysis learns to identify the veracity of a claim and its relevant evidences. |
| Approach: | They propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way. |
| Outcome: | The proposed model can achieve competitive results on the FEVER dataset while using significantly fewer parameters. |
Learning to Decode Collaboratively with Multiple Language Models (2024.acl-long)
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| Challenge: | Using a latent variable model, multiple large language models can be trained to collaborate at the token level. |
| Approach: | They propose a method to teach multiple large language models to collaborate by interleaving their generations at the token level. |
| Outcome: | The proposed method improves on instruction-following, domain-specific QA, and reasoning tasks and shows that the model trained with the method exhibits several interesting collaboration patterns. |
Addressing Posterior Collapse with Mutual Information for Improved Variational Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing variational inference models ignore their latent variables, a phenomenon called posterior collapse. |
| Approach: | They propose a new loss function for conditional variational autoencoders that counteracts posterior collapse by using a modified evidence lower bound objective and a factorized decoder. |
| Outcome: | The proposed model yields improved translation quality compared to existing models on WMT RoEn and DeEn. |