Adaptive Language Modeling Using Minimum Discriminant Estimation

By Stephen Della Pietra, V. D. Pietra, Robert L. Mercer, Salim Roukos. 1992

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In Natural Language Processing

Citations in the corpus

Unbounded cache model for online language modeling with open vocabulary (2017, background): “Della Pietra et al.[15] adapt the cache to a general n -gram model such that it satisﬁes marginal constraints obtained from the current document.”
Improving Neural Language Models with a Continuous Cache (2016, background): “Della Pietra et al. (1992) adapt the cache to a general n -gram model such that it satisﬁes marginal constraints obtained from the current document.”
Agnostic Federated Learning (2019, background): “…2007; Blitzer et al., 2007; Jiang and Zhai, 2007; Raju et al., 2018), speech recognition (Legetter and Woodland, 1995; Gauvain and Chin-Hui, 1994; Pietra et al., 1992; Rosenfeld, 1996; Jelinek, 1998; Roark and Bacchiani, 2003), and computer vision (Mart´ınez, 2002) A problem more closely related…”
Domain Adaptation: Learning Bounds and Algorithms (2009, methodology): “Similar techniques and other more reﬁned ones have been used for training maximum entropy models for language modeling or conditional models (Pietra et al., 1992; Jelinek, 1998; Chelba & Acero, 2006; Daum´e III & Marcu, 2006).”
Multiple Source Adaptation and the Rényi Divergence (2009, background): “This problem arises in a variety of natu-ral language processing tasks such parsing, statistical language modeling, text classiﬁcation [15, 7, 16, 9, 10, 4, 6], or speech processing [11, 8, 14] and computer vision [13] tasks, as well as in many other applications.”
Domain adaptation and sample bias correction theory and algorithm for regression (2014, background): “A more challenging problem of domain adaptation arises in a variety of applications, including natural language processing, speech processing, or computer vision [12, 4, 17, 18, 29, 30, 21].”
Two decades of statistical language modeling: where do we go from here? (2000, background): “Although this is a signiﬁcant improvement on such a difﬁcult corpus, it is nonetheless disappointing considering the amount of data involved.”
Shrinking Exponential Language Models (2009, methodology): “Della Pietra et al. (1992) introduce the idea of minimum discrimination information distributions.”
Domain Adaptation in Regression (2011, background): “But, a more challenging problem of domain adaptation arises in a variety of applications, including natural language processing, speech processing, or computer vision [7, 3, 9, 10, 17, 18, 12].”
Generalizing and Hybridizing Count-based and Neural Language Models (2016, background): “Also conceptually similar is work on adaptation of n-gram LMs, which start with n-gram probabilities (Della Pietra et al., 1992; Kneser and Steinbiss, 1993; Rosenfeld, 1996; Iyer and Ostendorf, 1999) and adapt them based on the distribution of the current document, albeit in a linear model.”
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches (2017, methodology): “Della Pietra et al. (1992) introduce a MaxEnt-based cache model by integrating a cache into a smoothed trigram language model, reporting reduction in both perplexity and word error rates.”