Written by

on

on

# Compositional Language Understanding with Text-based Relational Reasoning

# Study of Reasoning

## Inductive Logic Programming

## Relational Reasoning

## Propositional Satisfiability (SAT Solver)

# Proposal: CLUTRR

In the dataset, the task is to learn the compositional relations directly from text. If the model learns compositional elements, it should be able to re-use it to solve larger problems.

**Dataset construction**:

- Create a family of relation graphs where nodes are entities and edges are relations.
- Choose any two nodes, sample a path between them.
- Replace the edges with relations which are chosen randomly from a dictionary of templates.
- Predict the relation between the start and the sink/end of the path.

2018-12-07-compositionalQA-dataset.jpg

**Add noise as distractors**

- For a path with k relations and k+1 nodes, add
*m*distractor sentences per node. - Distractor sentence is an explanation of attributes which are not relevant to reasoning such as “Sam likes to play soccer.”

**Generalization in CLUTRR**

- Explicitly control the number of relations (k) in a path.
- Train on smaller number (k=3) and test on larger numbers (k=4,5).
- Generalizations are expressed as picking up compositional elements of relations from k=3, and then generalize them on k=4,5..
- Training and testing have
*different distributions*.

**Setup**

- Story/Input sentences: $S=(s_1,s_2,…,s_n)$ where $s_i=(w_1,..,w_m)$
- Subset of words $w_i$ are entities = ${e_1,..,e_n}$ which are anonymized in cloze-style.
- Each sentence $s_i$ describes a relation R.
- Predict relation between a pair of query entities $(e_i,e_j)$