A Natural Language Frame Semantic Parser


The figure below illustrates SLING recognizing mentions of entities (e.g. people, places, or events), measurements (e.g. dates or distances), and other concepts (e.g. verbs), and placing them in the correct semantic roles for the verbs in the input. The word predicted evokes the most dominant sense of the verb “predict”, denoted as a PREDICT-01 frame. Additionally, this frame also has interactions (slots) with who made the prediction (denoted via the ARG0 slot, which points to the PERSON frame for people) and what was being predicted (denoted via ARG1, which links to the EVENT frame for Black Monday). Frame semantic parsing is the task of producing a directed graph of such frames linked through slots.

Although the example above is fairly simple, frame graphs are powerful enough to model a variety of complex semantic annotation tasks. For starters, frames provide a convenient way to bring together language-internal and external information types (e.g. knowledge bases). This can then be used to address complex language understanding problems such as reference, metaphor, metonymy, and perspective. The frame graphs for these tasks only differ in the inventory of frame types, roles, and any linking constraints.

SLING
SLING trains a recurrent neural network by optimizing for the semantic frames of interest.
The internal learned representations in the network’s hidden layers replace the hand-crafted feature combinations and intermediate representations in pipelined systems. Internally, SLING uses an encoder-decoder architecture where each input word is encoded into a vector using simple lexical features like the raw word, its suffix(es), punctuation etc. The decoder uses that representation, along with recurrent features from its own history, to compute a sequence of transitions that update the frame graph to obtain the intended frame semantic representation of the input sentence. SLING trains its model using TensorFlow and DRAGNN.

The animation below shows how frames and roles are incrementally added to the under-construction frame graph using individual transitions. As discussed earlier with our simple example sentence, SLING connects the VERB and EVENT frames using the role ARG1, signifying that the EVENT frame is the concept being predicted. The EVOKE transition evokes a frame of a specified type from the next few tokens in the text (e.g. EVENT from Black Monday). Similarly, the CONNECT transition links two existing frames with a specified role. When the input is exhausted and the last transition (denoted as STOP) is executed, the frame graph is deemed as complete and returned to the user, who can inspect the graph to get the semantic meaning behind the sentence.

One key aspect of our transition system is the presence of a small fixed-size attention buffer of frames that represents the most recent frames to be evoked or modified, shown with the orange boxes in the figure above. This buffer captures the intuition that we tend to remember knowledge that was recently evoked, referred to, or enhanced. If a frame is no longer in use, it eventually gets flushed out of this buffer as new frames come into picture. We found this simple mechanism to be surprisingly effective at capturing a large fraction of inter-frame links.

Next Steps
The illustrative experiment above is just a launchpad for research in semantic parsing for tasks such as knowledge extraction, resolving complex references, and dialog understanding. The SLING release on Github comes with a pre-trained model for the task we illustrated, as well as examples and recipes to train your own parser on either the supplied synthetic data or your own data. We hope the community finds SLING useful and we look forward to engaging conversations about applying and extending SLING to other semantic parsing tasks.

Acknowledgements
The research described in this post was done by Michael Ringgaard, Rahul Gupta, and Fernando Pereira. We thank the Tensorflow and DRAGNN teams for open-sourcing their packages, and various colleagues at DRAGNN who helped us with multiple aspects of SLING’s training setup.



1 Charles J. Fillmore. 1982. Frame semantics. Linguistics in the Morning Calm, pages 111–138.

Until recently, most practical natural language understanding (NLU) systems used a pipeline of analysis stages, from part-of-speech tagging and dependency parsing to steps that computed a semantic representation of the input text. While this facilitated easy modularization of different analysis stages, errors in earlier stages would have cascading effects in later stages and the final representation, and the intermediate stage outputs might not be relevant on their own. For example, a typical pipeline might perform the task of dependency parsing in an early stage and the task of coreference resolution towards the end. If one was only interested in the output of coreference resolution, it would be affected by cascading effects of any errors during dependency parsing.

Today we are announcing SLING, an experimental system for parsing natural language text directly into a representation of its meaning as a semantic frame graph. The output frame graph directly captures the semantic annotations of interest to the user, while avoiding the pitfalls of pipelined systems by not running any intermediate stages, additionally preventing unnecessary computation. SLING uses a special-purpose recurrent neural network model to compute the output representation of input text through incremental editing operations on the frame graph. The frame graph, in turn, is flexible enough to capture many semantic tasks of interest (more on this below). SLING’s parser is trained using only the input words, bypassing the need for producing any intermediate annotations (e.g. dependency parses).

SLING provides fast parsing at inference time by providing (a) an efficient and scalable frame store implementation and (b) a JIT compiler that generates efficient code to execute the recurrent neural network. Although SLING is experimental, it achieves a parsing speed of >2,500 tokens/second on a desktop CPU, thanks to its efficient frame store and neural network compiler. SLING is implemented in C++ and it is available for download on GitHub. The entire system is described in detail in a technical report as well.

Frame Semantic Parsing
Frame Semantics [1] represents the meaning of text — such as a sentence — as a set of formal statements. Each formal statement is called a frame, which can be seen as a unit of knowledge or meaning, that also contains interactions with concepts or other frames typically associated with it. SLING organizes each frame as a list of slots, where each slot has a name (role) and a value which could be a literal or a link to another frame. As an example, consider the sentence:

“Many people now claim to have predicted Black Monday.”

The figure below illustrates SLING recognizing mentions of entities (e.g. people, places, or events), measurements (e.g. dates or distances), and other concepts (e.g. verbs), and placing them in the correct semantic roles for the verbs in the input. The word predicted evokes the most dominant sense of the verb “predict”, denoted as a PREDICT-01 frame. Additionally, this frame also has interactions (slots) with who made the prediction (denoted via the ARG0 slot, which points to the PERSON frame for people) and what was being predicted (denoted via ARG1, which links to the EVENT frame for Black Monday). Frame semantic parsing is the task of producing a directed graph of such frames linked through slots.

Although the example above is fairly simple, frame graphs are powerful enough to model a variety of complex semantic annotation tasks. For starters, frames provide a convenient way to bring together language-internal and external information types (e.g. knowledge bases). This can then be used to address complex language understanding problems such as reference, metaphor, metonymy, and perspective. The frame graphs for these tasks only differ in the inventory of frame types, roles, and any linking constraints.

SLING
SLING trains a recurrent neural network by optimizing for the semantic frames of interest.
The internal learned representations in the network’s hidden layers replace the hand-crafted feature combinations and intermediate representations in pipelined systems. Internally, SLING uses an encoder-decoder architecture where each input word is encoded into a vector using simple lexical features like the raw word, its suffix(es), punctuation etc. The decoder uses that representation, along with recurrent features from its own history, to compute a sequence of transitions that update the frame graph to obtain the intended frame semantic representation of the input sentence. SLING trains its model using TensorFlow and DRAGNN.

The animation below shows how frames and roles are incrementally added to the under-construction frame graph using individual transitions. As discussed earlier with our simple example sentence, SLING connects the VERB and EVENT frames using the role ARG1, signifying that the EVENT frame is the concept being predicted. The EVOKE transition evokes a frame of a specified type from the next few tokens in the text (e.g. EVENT from Black Monday). Similarly, the CONNECT transition links two existing frames with a specified role. When the input is exhausted and the last transition (denoted as STOP) is executed, the frame graph is deemed as complete and returned to the user, who can inspect the graph to get the semantic meaning behind the sentence.

One key aspect of our transition system is the presence of a small fixed-size attention buffer of frames that represents the most recent frames to be evoked or modified, shown with the orange boxes in the figure above. This buffer captures the intuition that we tend to remember knowledge that was recently evoked, referred to, or enhanced. If a frame is no longer in use, it eventually gets flushed out of this buffer as new frames come into picture. We found this simple mechanism to be surprisingly effective at capturing a large fraction of inter-frame links.

Next Steps
The illustrative experiment above is just a launchpad for research in semantic parsing for tasks such as knowledge extraction, resolving complex references, and dialog understanding. The SLING release on Github comes with a pre-trained model for the task we illustrated, as well as examples and recipes to train your own parser on either the supplied synthetic data or your own data. We hope the community finds SLING useful and we look forward to engaging conversations about applying and extending SLING to other semantic parsing tasks.

Acknowledgements
The research described in this post was done by Michael Ringgaard, Rahul Gupta, and Fernando Pereira. We thank the Tensorflow and DRAGNN teams for open-sourcing their packages, and various colleagues at DRAGNN who helped us with multiple aspects of SLING’s training setup.



1 Charles J. Fillmore. 1982. Frame semantics. Linguistics in the Morning Calm, pages 111–138.


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