Papers › Revisiting Simple Neural Probabilistic Language Models

Revisiting Simple Neural Probabilistic Language Models

8 Apr 2021NAACL 2021 4arXiv:2104.03474archive 2025-07-28

Simeng Sun, Mohit Iyyer

Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements. In this paper, we revisit the neural probabilistic language model (NPLM) of~\citet{Bengio2003ANP}, which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks. Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. Inspired by this result, we modify the Transformer by replacing its first self-attention layer with the NPLM's local concatenation layer, which results in small but consistent perplexity decreases across three word-level language modeling datasets.

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Tasks

Language ModelingLanguage ModellingWord Embeddings

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling WikiText-103 Transformer-N Number of params 148M #59 of 89 Archive leaderboard report
Language Modelling WikiText-103 Transformer-N Test perplexity 25.2 #59 of 89 Archive leaderboard report
Language Modelling WikiText-103 Transformer-N Validation perplexity 24.1 #59 of 89 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionNeural Probabilistic Language ModelPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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