Knowledge Vault 2/15 - ICLR 2014-2023
Hal Daumé III ICLR 2015 - Keynote - Algorithms that Learn to Think on their Feet
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

graph LR classDef nlp fill:#f9d4d4, font-weight:bold, font-size:14px; classDef imitation fill:#d4f9d4, font-weight:bold, font-size:14px; classDef ilp fill:#d4d4f9, font-weight:bold, font-size:14px; classDef incomplete fill:#f9f9d4, font-weight:bold, font-size:14px; A[Hal Daumé III
ICLR 2015] --> B[NLP: deep text
understanding 1] B --> C[Language:
ambiguous, humorous 2] B --> D[Fast NLP:
trained predictors 3] D --> E[Parsing:
analyzes sentences 4] E --> F[Dynamic features:
speeds classification 5] A --> G[DAgger:
trains policies 6] G --> H[Oracle: incremental
feedback better 7] H --> I[Parsing:
sped up, accurate 8] A --> J[Interpretation:
real-time translation 9] J --> K[Waiting, predicting,
committing decisions 10] A --> L[Quiz bowl:
incremental questions 11] L --> M[Quiz AI:
prediction, buzzing 12] B --> N[Compositionality:
phrase meanings 13] N --> O[Recursive NNs:
semantic modeling 14] O --> P[Quiz models:
ensemble best 15] A --> Q[NLP as ILPs:
test-time search 16] Q --> R[B&B search: efficiency 17] R --> S[B&B policy:
incumbent, unpromising, adaptive 18] S --> T[B&B learned:
near-optimal, few nodes 19] A --> U[Incomplete reasoning:
observe, wait 20] A --> V[Imitation: learns
sequential decisions 21] class B,C,D,E,F,N,O,P nlp; class G,H,I,J,K,L,M,V imitation; class Q,R,S,T ilp; class U incomplete;


1.-Natural Language Processing (NLP) aims for deep understanding of text to build systems for tasks like translation, information extraction, and question answering.

2.-Language is challenging due to widespread ambiguity, as illustrated by humorous newspaper headlines with multiple interpretations.

3.-Fast NLP systems can be built by training fancy learning algorithms on data to produce optimized predictors that approximate hand-built heuristics.

4.-Dependency parsing analyzes the linguistic structure of sentences but standard approaches using many features are computationally expensive, especially for long sentences.

5.-Dynamic feature selection speeds up classification by sequentially choosing a subset of features to use based on the example being classified.

6.-The DAgger algorithm trains policies by aggregating datasets of trajectories generated by the expert policy and trained policies over multiple iterations.

7.-Having the oracle policy be too good is problematic for learning - a coaching approach that gives incremental feedback works better.

8.-Applying dynamic feature selection and imitation learning to dependency parsing substantially speeds it up while maintaining accuracy.

9.-Simultaneous interpretation requires translating speech from one language to another in real-time, which is challenging due to word order differences between languages.

10.-Deciding when to wait, predict or commit to a translation can be formulated as a sequential decision making problem solvable by imitation learning.

11.-In the quiz bowl trivia competition, questions are read incrementally and players must decide when to buzz in with an answer.

12.-An AI system for playing quiz bowl can use an answer prediction model combined with a buzzing policy trained via imitation learning.

13.-Capturing compositional semantics, such as inferring the meaning of multi-word phrases, is a key challenge in question answering.

14.-Recursive neural networks that combine vector representations of words and phrases show promise for modeling semantic compositionality.

15.-On history and literature questions, models trained on quiz bowl data outperform those trained on Wikipedia, and ensembles perform best.

16.-Many NLP problems can be formulated as integer linear programs (ILPs) to be solved at test time using branch-and-bound search.

17.-The search strategy used in branch-and-bound has a big impact on efficiency and can be optimized using imitation learning.

18.-A learned branch-and-bound policy should find good incumbent solutions early, identify unpromising nodes, and adapt its strategy based on tree position.

19.-On ILP benchmarks, branch-and-bound with a learned policy achieves near-optimal results while exploring a fraction of the nodes expanded by Gurobi.

20.-Reasoning with incomplete information by learning when to observe features or wait for more input can improve efficiency and enable new capabilities.

21.-Imitation learning, by learning policies from expert demonstrations, provides a general framework for learning to make sequential decisions in NLP and beyond.

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