Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRevisiting Simple Neural Probabilistic Language Models
إعادة النظر في نماذج لغة الاحتمالية البسيطة العصبية
370
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
كان التقدم المحرز الأخير في نمذجة اللغة مدفوعة ليس فقط بالتقدم في البنيات العصبية، ولكن أيضا من خلال تحسين الأجهزة والتحسين.في هذه الورقة، نؤيد نموذج اللغة الاحتمالية العصبية (NPLM) من بنغيو وآخرون.(2003)، والتي تسلسل ببساطة تضمين كلمة داخل نافذة ثابتة ويمرر النتيجة من خلال شبكة تغذية إلى الأمام للتنبؤ بالكلمة التالية.عند القياس حتى الأجهزة الحديثة، يؤدي هذا النموذج (على الرغم من قيودها العديدة) أفضل بكثير مما كان متوقعا عن معايير نموذج اللغة على مستوى Word.يكشف تحليلنا أن NPLM يحقق حيرة أقل من محول الأساس مع سياقات مدخلات قصيرة ولكن تكافح للتعامل مع تبعيات طويلة الأجل.مستوحاة من هذه النتيجة، نقوم بتعديل المحول عن طريق استبدال طبقة انتباهي أول مع طبقة التسلسل المحلية في NPLM، مما يؤدي إلى انخفاض حيرة صغيرة ولكنها ثابتة عبر مجموعات بيانات نمذجة لغة مستوى الكلمات.
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 Bengio et al. (2003), 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.
References used
https://aclanthology.org/
rate research
Read More
Policy gradient algorithms have found wide adoption in NLP, but have recently become subject to criticism, doubting their suitability for NMT. Choshen et al. (2020) identify multiple weaknesses and suspect that their success is determined by the shap
e of output distributions rather than the reward. In this paper, we revisit these claims and study them under a wider range of configurations. Our experiments on in-domain and cross-domain adaptation reveal the importance of exploration and reward scaling, and provide empirical counter-evidence to these claims.
Pretrained language models have served as the backbone for many state-of-the-art NLP results. These models are large and expensive to train. Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining lead
s to improved performance on downstream tasks. We explore alternatives to full-scale task-specific pretraining of language models through the use of adapter modules, a parameter-efficient approach to transfer learning. We find that adapter-based pretraining is able to achieve comparable results to task-specific pretraining while using a fraction of the overall trainable parameters. We further explore direct use of adapters without pretraining and find that the direct fine-tuning performs mostly on par with pretrained adapter models, contradicting previously proposed benefits of continual pretraining in full pretraining fine-tuning strategies. Lastly, we perform an ablation study on task-adaptive pretraining to investigate how different hyperparameter settings can change the effectiveness of the pretraining.
We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability
the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
Emotion Classification is the task of automatically associating a text with a human emotion. State-of-the-art models are usually learned using annotated corpora or rely on hand-crafted affective lexicons. We present an emotion classification model th
at does not require a large annotated corpus to be competitive. We experiment with pretrained language models in both a zero-shot and few-shot configuration. We build several of such models and consider them as biased, noisy annotators, whose individual performance is poor. We aggregate the predictions of these models using a Bayesian method originally developed for modelling crowdsourced annotations. Next, we show that the resulting system performs better than the strongest individual model. Finally, we show that when trained on few labelled data, our systems outperform fully-supervised models.
When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of re
cent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
