| Challenge: | Existing methods for selecting training data for recurrent neural network language models are limited by computational costs. |
| Approach: | They propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNN) language models. |
| Outcome: | The proposed approach outperforms models trained on the Billion Word and Wikitext-103 benchmark corpora. |
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| Challenge: | a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus is effective, and more time-efficient than training on ngrams. |
| Approach: | They propose a technique that uses large corpus n-gram statistics as a regularizer for training on a smaller corpus. |
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Target-Aware Language Modeling via Granular Data Sampling (2024.emnlp-main)
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Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
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On Importance Sampling-Based Evaluation of Latent Language Models (2020.acl-main)
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| Challenge: | Existing approaches to evaluate language models using latent structures are intractable as they require marginalizing over the latent space. |
| Approach: | They propose to use importance sampling to evaluate latent language models . they elucidate subtle differences in how importance sampling is applied . |
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Unsupervised training data re-weighting for natural language understanding with local distribution approximation (2022.emnlp-industry)
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| Challenge: | a distribution mismatch between offline training and live data can cause biases . cyclic seasonality shifts, and changing pool of users can contribute to this problem . |
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Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models (D18-1)
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| Challenge: | Recurrent neural network language models (RNNs) only estimate probabilities for complete sequences of text, whereas some applications require context-independent phrase probabilities instead. |
| Approach: | They propose a method to alter the RNNLM training to make it more accurate at marginal estimation. |
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Dependency-based Mixture Language Models (2022.acl-long)
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| Challenge: | Existing models to incorporate syntactic structures into neural language models have relied heavily on elaborate components for a specific language model, which makes them unwieldy in practice to fit into other models. |
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Direct Output Connection for a High-Rank Language Model (D18-1)
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| Challenge: | Neural network language models have played a central role in recent natural language processing advances. |
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What data should I include in my POS tagging training set? (2025.findings-emnlp)
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| Challenge: | POS tagging is a crucial task for descriptive linguistics and language documentation . POS tags are not available in all languages, but are used for training sets for understudied languages . |
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Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation (P19-1)
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| Challenge: | Existing studies show that training on a single related language is more effective than using all data. |
| Approach: | They propose an efficient algorithm that first samples a target sentence, and then conditionally samples its source sentence. |
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Experimenting with Power Divergences for Language Modeling (D19-1)
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| Challenge: | Language models are an important component in many NLP tasks, where they provide prior knowledge on the language used. |
| Approach: | They propose to use power divergences to prioritize learning on frequent or rare words . they use a sample-based objective to approximate a softmax and noise-constrained estimate . |
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