From Dependency Parsing to Imitation Learning
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1 From Dependency Parsing to Imitation Learning CMSC 723 / LING 723 / INST 725 Marine Carpuat Fig credits: Joakim Nivre, Yoav Goldberg, Hal Daume III
2 Today s topics: Addressing compounding error Improving on gold parse oracle Research highlight: [Goldberg & Nivre, 2012] Imitation learning for structured prediction CIML ch 18
3 Improving the oracle in transition-based dependency parsing Issues with oracle we ve used so far Based on configuration sequence that produces gold tree What if there are multiple sequences for a single gold tree? How can we recover if the parser deviates from gold sequence? Goldberg & Nivre [2012] propose an improved oracle
4 Exercise: which of these transition sequences produces the gold tree on the left?
5 Stack Buffer Dependency Arcs Arc from position j to position i, with dependency label l
6 Which of these transition sequences does the oracle algorithm produce?
7 Improving the oracle in transition-based dependency parsing Issues with oracle we ve used so far Based on configuration sequence that produces gold tree What if there are multiple sequences for a single gold tree? How can we recover if the parser deviates from gold sequence? Goldberg & Nivre [2012] propose an improved oracle
8 SHIFT At test time, suppose the 4 th transition predicted is SHIFT instead of RAIOBJ What happens if we apply the oracle next?
9 Measuring distance from gold tree Labeled attachment loss: number of arcs in gold tree that are not found in the predicted tree Loss = 3 Loss = 1
10 Improving the oracle in transition-based dependency parsing Issues with oracle we ve used so far Based on configuration sequence that produces gold tree What if there are multiple sequences for a single gold tree? How can we recover if the parser deviates from gold sequence? Goldberg & Nivre [2012] propose an improved oracle
11 Proposed solution: 2 key changes to training algorithm Any transition that can possibly lead to a correct tree is considered correct Explore non-optimal transitions
12 Proposed solution: 2 key changes to training algorithm
13 Defining the cost of a transition Loss difference between minimum loss trees achievable before and after transition Loss for trees nicely decomposes into losses for arcs We can compute transition cost by counting gold arcs that are no longer reachable after transition
14 Today s topics Addressing compounding error Improving on gold parse oracle Research highlight: [Goldberg & Nivre, 2012] Imitation learning for structured prediction CIML ch 18
15 Imitation Learning aka learning by demonstration Sequential decision making problem At each point in time t Receive input information x t Take action a t Suffer loss l t Move to next time step until time T Goal learn a policy function f(x t ) = y t That minimizes expected total loss over all trajectories enabled by f
16 Supervised Imitation Learning
17 Supervised Imitation Learning Problem with supervised approach: Compounding error
18 How can we train system to make better predictions off the expert path? We want a policy f that leads to good performance in configurations that f encounters A chicken and egg problem Can be addressed by iterative approach
19 DAGGER: simple & effective imitation learning via Data AGGregation Requires interaction with expert!
20 When is DAGGER used in practice? Interaction with expert is not always possible Classic use case Expert = slow algorithm Use DAGGER to learn a faster algorithm that imitates expert Example: game playing where expert = brute-force search in simulation mode But also structured prediction
21 Sequence labeling via imitation learning What is the expert here? Given a loss function (e.g., Hamming loss) Expert takes action that minimizes long-term loss Output prefix at time t Loss of best reachable output starting with prefix y a When expert can be computed exactly, it is called an oracle Key advantages Can define features No restriction to Markov features
22 Today s topics Improving on gold parse oracle Research highlight: [Goldberg & Nivre, 2012] Imitation learning for structured prediction CIML ch 18
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