It’s a bit of an old post August last year – but they seem to be pushing it at the moment via email promo…
Traditional search technology matches results based only on the keywords and synonyms in a query. But in the law, where language is highly nuanced and arguments are often based on concepts and analogies, keyword searches fall woefully short. Casetext’s Parallel Search changes the game by taking a natural language sentence from the user and returning passages from the law that mean the same thing even when they have no words in common. You can try for yourself at http://parallelsearch.compose.law.
For example, an attorney can enter a proposition of law like “The right to protest is fundamental to American democracy.” and Parallel Search returns the following in federal circuit courts:
Although all three of these top results are statements written by judges that perfectly support the proposition, search techniques that rely on keyword matches would not be able to locate these results. The first result shares only 2 important words with the query — “fundamental” and “right” — which appear in the same sentence in 190,000 cases. For the second two results, which only share the word “right” with the query, word matching is even less sufficient; the word “right” appears in over 4 million cases.
So, how was Parallel Search able to do it? Parallel Search uses advanced machine learning techniques to extract concepts from sentences, and find matches based on concepts, rather than keywords. In this article, I will explain, at a high level, how that machine learning works.
Neural Transformers
The fundamental building block of Parallel Search is a type of artificial neural network called the Transformer. An artificial neural network is a machine learning technique loosely inspired by the structure of neurons in the brain. Neural networks are structured as many layers of pattern recognizers stacked on top of each other. The first layer takes in the input data, finds simple patterns in it, and passes information about those patterns on to the next layer. Each successive layer is able to recognize increasingly complex patterns.
The following are patterns recognized by successive layers in a computer vision neural network (which we’re using as an example here because the patterns they recognize are much easier to visualize).
As you can see, the early layers on the left hand side recognize simple patterns, like variously angled lines, and color gradients. As we move through the layers to the right, the patterns are created by combining the patterns from the layer before into more complex shapes. At the rightmost layer, you can see quite complex shapes emerging, like wheels, and honeycombs.
The patterns that neural networks recognize are not pre-programmed by human programmers. Instead, they are “learned” by adjusting the weights of connections between layers in the network. This is done by running large numbers of examples of inputs and the desired outputs through the network and adjusting the weights of the connections a little bit at a time to make the output of the network progressively closer to the desired output.
Transformers are a type of neural network that is good at doing pattern recognition on text instead of images. For more detail on Transformers, you can read our prior blog post where we discussed them here.
Neural Transformers for Semantic Search
Casetext is one of the first companies in the world to apply neural transformers to the problem of natural language semantic search. Our system involves a neural transformer model that was pre-trained on the entire body of U.S. judicial opinions.
The naive way to try to do semantic search with neural transformers is to feed each possible result sentence along with the query to the model and train it to decide, is this a good result for this query or not? But checking the whole common law, consisting of 700 million sentences, would take days to return results for each search query.
Instead, we follow a multi-step process in which we train a transformer to create representations of legal sentences in the form of vectors (essentially, a list of numbers that represents the sentence for purposes of comparison). This diagram shows the high level architecture of the system. We will go through each component piece-by-piece.
Transformer Ranker Model
We use this transformer to create vector representations of every sentence in the law and store those vectors ahead of time in a vector nearest neighbor index. When a user enters a query, we use the same transformer to create a representation of the query sentence, and then we look up its closest matches in the vector nearest neighbor index.
This transformer is based on a model that has been pre-trained on all judicial opinions, but it must be finetuned for the task of creating vector representations of sentences that can be fruitfully compared to each other. We train it by labeling the sentence pairs we feed the network as 1 when the sentences are similar, and 0 when they’re dissimilar, and we train the model to produce that desired output. The end result is a model that will produce vector representations of sentences that are optimized for this type of sentence comparison.
Vector Nearest Neighbor Index
Once we have vector representations of sentences that can be compared with a distance metric, we still aren’t done. We could compare the query sentence vector to every other possible result vector, but it would still be far too slow to be commercially useful.
To speed things up further, we store the possible result vectors (one vector for each sentence in the law) in a special type of index designed for finding the nearest neighbors of a query vector, without comparing the query vector to every possible result vector. Vector nearest neighbor indices accomplish this by partitioning the possible result vectors into boxes, which contain smaller boxes, which themselves contain smaller boxes, and so on.
When a query vector is given to the index, it’s compared to a representative of the highest-level (largest) boxes. The closest box to the query vector is selected, and then the same process is repeated for the boxes within that box, and so on until a box containing only a few vectors is found. This small group is then compared to the query vector and the closest ones are taken as our results.
Transformer Reranker Model
The lookup in the vector nearest neighbor index yields a set of results ranked by proximity to the sentence vector representing the query. These results are usually good on their own, but we found that with an additional step, we could make them even better. We finetuned a second transformer that can take as input both the query sentence and a result sentence and decide how good a match they are. It essentially takes the results from the prior steps and rearranges their order so that the very best results are more likely to be at the top. We call this a “re-ranker,” and we found that it substantially improved the results.
Putting it all together
We use two different neural transformer models that are both pre-trained on the whole body of U.S. common law and then finetuned to their separate tasks, one as a creator of useful sentence representations, and the other as a re-ranker that evaluates query-sentence pair matches.
To our knowledge, Casetext’s Parallel Search is the most advanced natural language semantic search tool available in the legal industry today, and our team is constantly making improvements. We invite you to try our tool, and we very much hope it helps you achieve justice for your clients. You can try it out by clicking here.
For more information about Parallel Search, or to see how Parallel Search powers Compose, brief-drafting technology developed by Casetext, reach out to [email protected].
