From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood (1704.07926v1)

Abstract: Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.

• The paper bridges reinforcement learning and maximum marginal likelihood by establishing that both optimize closely related objectives while mitigating spurious programs.
• It introduces a randomized beam search strategy that enhances exploration and prevents premature convergence on incorrect programs.
• The novel meritocratic update rule applied to a neural semantic parser achieves over 30% accuracy improvement on the SCONE dataset.

From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood

The paper "From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood," authored by researchers from Stanford University, concentrates on developing a semantic parser capable of translating natural language utterances into executable programs. Particular attention is given to scenarios where supervision is indirect; that is, the correct execution result is provided, but not the program itself.

Objective and Approach

A core challenge addressed by the paper is the identification and avoidance of spurious programs—incorrect programs that produce the correct output coincidentally. This task is conceptualized as a sequential decision-making process, leveraging two common learning paradigms: Reinforcement Learning (RL) and Maximum Marginal Likelihood (MML). The paper presents a novel algorithm, RandoMer, which synthesizes elements of RL and MML to elegantly address the exploration in program spaces and parameter updates.

Key Contributions

1. Connection Between RL and MML: The authors establish that RL and MML optimize closely related objectives, albeit with different gradient estimation strategies. MML is shown to handle spurious programs better due to the renormalization over reward-earning programs rather than all programs.
2. Randomized Beam Search: The paper introduces a new exploration strategy. Instead of the deterministic nature of traditional beam search, randomized beam search introduces an element of randomness akin to epsilon-greedy sampling, improving exploration and reducing the likelihood of prematurely converging on short, spurious programs.
3. Meritocratic Update Rule: A new update rule modifies the gradient to more evenly distribute model probability across programs producing the correct output. This rule is pivotal in reducing overfitting to spurious programs and encouraging the discovery of truly correct programs.
4. Neural Semantic Parser: Application of the learning algorithm to train a neural semantic parser yields significant advantages over existing methods demonstrated on the SCONE dataset. This dataset includes tasks across various domains characterized by context-dependent semantic parsing challenges.

Numerical Results

The algorithm has demonstrated superior accuracy on SCONE, outperforming both standard RL and MML methods. On the Scene domain, characterized by a complex referencing strategy, the proposed method achieved over 30% improvement in accuracy compared to prior state-of-the-art techniques.

Implications and Future Work

The implications of this research are multifaceted. Practically, it represents advancements in semantic parsing that can be expanded to broader applications requiring natural language to program translation. Importantly, it offers a robust approach for handling partial supervision scenarios in AI development. Theoretically, the connection established between RL and MML offers a framework for further exploration into hybrid learning paradigms. Future developments might focus on dynamic adjustments to the meritocratic gradient scaling, exploring its potential in other domains, and expanding to more intricate program synthesis tasks.

The paper's methodological innovations challenge conventional boundaries between RL and MML, providing pathways for future research aimed at refining semantic parsers and unlocking nuanced understanding of sequential decision-making under indirect supervision. This research showcases a promising direction for AI in terms of merging methodologies to overcome traditional limitations.