Title: Large Margin Neural Language Models. Abstract: Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences. Trained on huge corpus, NLMs are pushing the limit of modeling accuracy. Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR). By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate. However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance. This work proposes an approach to adapt a generative NLM to a discriminative one. Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences. It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text. Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.