user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Write by Learning the Objective

Paper content:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.
Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.
We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.
In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.
Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.
Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.
Recurrent Neural Network (RNN) based language models such as Long Short-Term Memory Networks (LSTMs) BID6 and Gated Recurrent Units (GRUs) BID2 have achieved enormous success across a variety of language tasks due to their ability to learn fluency patterns in natural language BID8 BID10 BID17 .
When used as a generator, however, the quality of language generated from RNNs deviates drastically from that of human language.
While fluent, RNN-produced language displays several degenerate characteristics, favoring generic and contentless output that tends to be repetitive and self-contradictory.
These issues are especially prominent when RNNs are used for open-ended, long-form text generation, as illustrated in Figure 1 .RNNs
model the conditional probability P (x t |x 1 , ..., x t−1 ) of generating the next word x t given all previous words observed or generated. In theory
, this conditional model should be able to learn all crucial aspects of human language production, for example, that we don't normally repeat the same content over and over. In practice
, however, the learned conditional probability model often assigns higher probability to a repetitive, overly generic sentence than to higher quality sentences, as shown in Figure 1 . We postulate
that this is in part because the network architectures of RNN variants do not provide a strong enough inductive bias for the model to learn the complex communication goals pursued in human writing. In addition,
long-term context easily gets lost as it is explained away in the presence of more immediately relevant short-term context BID34 , and as gradients diminish over a long sequence BID21 . Consequently
, RNNs acquire relatively shallow and myopic patterns, which tend to only take advantage of a small fraction of the training set vocabulary BID9 . RNNs are thus
unable to generate language that matches the complexity and coherence of human generated text.Several methods in the literature attempt to mitigate these issues. Overly simple
and generic generation can be improved by using a diversity-boosting objective function BID24 BID28 . Repetitive generation
can be reduced by prohibiting recurrence of the same trigrams as a hard rule BID22 . Although such constraints
form a partial solution, they All in all, I would highly recommend this hotel to anyone who wants to be in the heart of the action, and want to be in the heart of the action. If you want to be in the
heart of the action, this is not the place for you. However, If you want to
be in the middle of the action, this is the place to be.Figure 1: A Trip Advisor review generated by an RNN based LM trained on over a million reviews.are generally too coarse and both penalize good behavior (e.g. reuse of an idiom) and fail to capture more complex bad behavior (e.g. paraphrasing of the same content again and again).Hand tailoring rules is
both time consuming and unstable across different generative scenarios, so we instead propose a general learning framework to construct a better decoding objective. Starting with a generatively
trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address limitations of the base RNN generator. Our learning framework therefore
generalizes over various existing modifications to the decoding objective. Our approach learns to overcome
the particular limitations of the RNN generator directly by incorporating language generated from RNNs as negative samples to discriminatively train several companion models, each specializing in a different aspect of Grice's Maxims of communication BID4 ).Empirical results demonstrate that
our learning framework is highly effective in converting a generic RNN language model into a substantially stronger generator. Human evaluation confirms that language
generated by our model is preferred over that of competitive baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.
Our work presents a unified learning framework that can learn to generate long, coherent text overcoming the limitations of RNNs as text generation models.
Our framework learns a decoding objective suitable for generation through a combination of sub-models that capture linguisticallymotivated qualities of good writing.
Our work makes a unique contribution that complements existing literature on long text generation that is predominantly based on seq-to-seq models with a large amount of in-domain training data; we demonstrate that the fluency of general RNN language models can be successfully guided to generate more lengthy and sensical text, which can be useful for domains where in-domain data is not sufficient to support seq-to-seq type training.We propose a general framework for learning a decoding objective in two parts: learning component models to rank candidate generations, which are motivated by different aspects of Grice's Maxims, and learning a weighing scheme that balances the influence of each of these scoring functions.
This framework is amenable to any number and kind of sub-objectives, allowing for multiple qualities of good writing to be modeled across diverse domains.
Human evaluation shows that the quality of the text produced by our model exceeds that of RNN baselines by a large margin and the generations score significantly higher on a Turing test evaluation.Context The location is amazingly convenient.
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I recommend walking to the san marco piazza on your first time ( make sure you wear comfortable shoes ).
Language Model The hotel is in a great location.
The room was clean and comfortable.
The staff was very friendly and helpful.
The breakfast was very good.
The staff was very friendly and helpful.
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I would recommend this hotel to anyone who is looking for a place to stay in venice.
I would recommend this hotel to anyone who is looking for a place to stay while in No Meta-Learning The staff were very friendly and helpful and the breakfast buffet Learn2Write The rooms are spacious and clean.
Breakfast is very good.
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The guys at the reception are amazing.
Very friendly and very helpful : ) ) what you want from a hotel in venice is a decent place to sleep, have a relaxing bath and some breakfast in the morning.
From then on you will be spending all your time in town anyway so fo me the abbazia hotel was an excellent choice and i will go back for sure.
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assistant: text: This paper proposes to improve RNN language model generation using augmented objectives inspired by Grice's maxims of communication.