---
# System prepended metadata

title: Note 0705

---

# Note 0705

## SRL
### Current idea
- capture data regularities encoded in dependency syntax
    - some SRL relations occurs in specfic syntactic structure
        - v-predicate: SUBJ dependency : Noun $\rightarrow$ v-predicate: A0 semantic : Noun
        - a simple biaffine classifier does not encode such tendencies explicitly
    - dependency syntax encodes a sort of prior complementing simple biaffine parser
    - encode the prior with a mixture model
        - mixture model component can encode prior for each SDP pattern
        - SDP pattern inducing similar semantic relations can be grouped together
        - we effectively have several clusters, each of which encodes one kind of SRL tendency 
### dependency-based SRL
#### Status: success
- ~0.6-1 F1 improvement over baseline 
- Greatest improvement comes from separating (0, 1) SDP pattern 
- Mixture model also recovers a similar cluster as i setted
    - (0,1) 
    - (1, 0) (1, 1)
    - (2, 0)
    - and others
#### other improvements
- Encoding information in shortest dependency path via LSTM
    - SDP pattern shown above can be considered as counting
    - LSTM can learn counting -> possibly generalize SDP patterns to a boarder range of syntactic features
    - Also, we can use go for the LSTM over SDP idea
    - !!! However, judging from score only, using LSTM seems to be worse

#### 
### span-based SRL
#### Status: fail
- almost no effect, or even worse than baseline
- The problem is that the strong regularities of dependency-SRL is lost in span-based SRL (when taking it as a sequence-tagging task)
    - dependency pattern only show clue to head words
    - span-based SRL requires tagging for the whole constituent (including the head and other words for that constituent)
```
0:DOCUMENT 1:PART 2:INDEX 3:WORD 4:POS 5:PARSE 6:LEMMA 7:FRAME 8:SENSE 9:SPEAKER 10:NE 11-N:ARGS N:COREF
bc/cctv/00/cctv_0001   0   0           This    DT  (TOP(S(NP*         -    -   -   Speaker#1        *   (ARG2*   (61
bc/cctv/00/cctv_0001   0   1            map    NN           *)        -    -   -   Speaker#1        *        *)   61)
bc/cctv/00/cctv_0001   0   2      reflected   VBD        (VP*    reflect  01   1   Speaker#1        *      (V*)    -
bc/cctv/00/cctv_0001   0   3            the    DT        (NP*         -    -   -   Speaker#1        *   (ARG1*     -
bc/cctv/00/cctv_0001   0   4       European    JJ           *         -    -   -   Speaker#1    (NORP)       *     -
bc/cctv/00/cctv_0001   0   5    battlefield    NN           *         -    -   -   Speaker#1        *        *     -
bc/cctv/00/cctv_0001   0   6      situation    NN           *))       -    -   -   Speaker#1        *        *)    -
bc/cctv/00/cctv_0001   0   7              .     .           *))       -    -   -   Speaker#1        *        *     -
```
- A better decoding method for span-based task will be needed
    - [span-selection](https://aclanthology.org/D18-1191.pdf)
    - chart-filling (maybe joint-constituency-spanSRL parsing?)
    - ...
